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Do Industry Differences Matter – IFRS Versus US GAAP?

Peng-Chia Chiu

Assistant Professor of Accounting

The Chinese University of Hong Kong

Morton Pincus

Dean’s Professor of Accounting

University of California, Irvine

Karen Zhou

Staff Accountant

PwC, London

September 10, 2016

Preliminary – Please do not quote without permission.

We thank workshop participants at the Chinese University of Hong Kong, University of California-Davis, participants in the UC

Irvine master’s course in accounting research and policy, and Stephen Campbell, Tiana Lehmer, Esther Rihawi, and Terry

Shevlin for helpful comments and suggestions on earlier drafts of this paper.

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Do Industry Differences Matter – IFRS Versus US GAAP?

ABSTRACT:

We assess the informativeness of earnings as reflected in long-window earnings response

coefficients (ERCs) of European Union (EU) firms relative to US firms; we do this overall and with

regard to specific accounting standards that differ between IFRS and US GAAP. The generally greater

managerial discretion over accounting policies under the more principles-based IFRS relative to the more

rules-oriented US GAAP can be expected to result in reported earnings under IFRS that either better

reflect underlying fundamentals and thus are more informative or yield less informative earnings due to

the likely greater opportunities for earnings management. While both IFRS and US GAAP are viewed as

high quality accounting regimes, an important difference between the two is that US GAAP tends to

reflect industry differences in its standards and guidance whereas that is generally not the case under

IFRS. We examine ERCs under IFRS vs. US GAAP cross-sectionally in the EU’s post-IFRS adoption

period (2005-13), and using a difference-in differences approach and identifying industries ex ante that

likely are most affected by given accounting policies, we gauge the informativeness of earnings for firms

in such impacted industries; we also include as additional controls EU and US firms in industries that are

likely not substantially impacted by the specific accounting policies we consider. We find (a) evidence

that overall mean ERCs are lower for IFRS vis-à-vis US firms; (b) evidence that, except in the initial

period of IFRS adoption, IFRS-based earnings generally are associated with higher ERCs for firms in

R&D-intensive industries, which ironically is more reflective of industry differences than under US

GAAP; (c) some evidence of lower ERCs for inventory costing under IFRS for firms in LIFO-intensive

industries, except that during the financial crisis period US GAAP earnings are less informative likely due

to LIFO inventory depletions; and some evidence of (d) more informative earnings for multiple-element

software revenue recognition under IFRS and (e) less informative earnings for lease-intensive firms under

IFRS. Moreover, the results generally suggest that country-level institutional factors and accounting

standards both impact the informativeness of earnings.

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Do Industry Differences Matter – IFRS Versus US GAAP?

I. Introduction

In this study we investigate whether different accounting regimes – International Financial

Reporting Standards (IFRS) vs. US Generally Accepted Accounting Principles (GAAP) make underlying

fundamentals significantly more distinct and apparent in reported earnings numbers. Using industry as a

proxy for firm fundamentals, we ask whether firms’ earnings under IFRS vs. US GAAP better reflect

underlying performance and as such whether reported earnings are more useful to investors.

Research has investigated the quality of financial accounting information generated under IFRS

relative to the information based on the GAAP of firms’ country of domicile. There is also a research

stream that assesses whether adopting IFRS is associated with greater comparability of accounting

information of firms from different countries. This latter research thrust includes work that compares non-

US firms’ accounting information prepared under IFRS with US firms’ accounting information reported

under US GAAP. Comparing IFRS and US GAAP is particularly important as these two accounting

regimes are widely viewed as high quality, and there has been a major effort for more than 15 years to

converge IFRS and US GAAP and move toward (if not achieve) a single set of high quality accounting

standards/guidelines that could be adopted by firms around the world.

The European Union (EU) mandated IFRS for EU firms, implemented it in 2005, and now more

than 100 jurisdictions worldwide have adopted at least some parts of IFRS (Barth 2015). In the US, by the

mid-2000s there was a seeming inevitability that US GAAP and IFRS would increasingly converge and

that US firms would have the option to use IFRS or US GAAP. At least as late as 2011 there was a

widespread belief that substantial convergence of IFRS and US GAAP would continue, if not accelerate,

and the possibility that IFRS would be mandated for US firms was quite real.1 It was expected that a

1 A March 2011 booklet (pp. 1-3), “US GAAP Convergence & IFRS: Effective dates and transition methods,” by PwC states:

“New accounting standards will be issued this year that will fundamentally change how companies account for revenues, leases,

and financial instruments.” “The Financial Accounting Standards Board and the International Accounting Standards Board…are

working on about a dozen joint projects designed to improve both US and international accounting standards. The scale of the

proposed changes and the potential impact on companies are unprecedented.” “Assuming the new standards on financial

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decision regarding IFRS use by US firms would be made by 2013, with some form of IFRS use by US

firms likely implemented in 2014 or 2015. This did not happen. Notwithstanding the dictate in Section

108 of the Sarbanes-Oxley Act of 2002 for the US to study moving towards convergence with, and

possible adoption of, a principles-based accounting system, the Financial Accounting Standards Board

(FASB) and the International Accounting Standards Board (IASB) have had somewhat mixed success at

converging their respective sets of standards, and the Securities and Exchange Commission (SEC), which

has the authority to decide on IFRS adoption by US firms, has continued to postpone its decision on

IFRS. The SEC’s chief accountant raised the question of IFRS use again in late 2014, suggesting that

convergence was still being considered, although more recent comments indicate that the FASB and

IASB are nearing the end of their joint convergence projects.2 SEC Chair Mary Jo White has reiterated

the importance for the SEC to state its current views regarding “the goal of a single set of high-quality

global accounting standards” (Tysiac 2015b).

IFRS reflects a predominantly principles-based orientation for financial accounting standards

whereas US GAAP takes a more rules-based, guidance approach. US GAAP is generally viewed as

allowing managers less discretion in implementing accounting policies than is typical under IFRS,

although US GAAP often provides industry-specific guidance that effectively enables firms in such

industries to follow differing guidance for implementing given accounting policies. The importance of

this is recognized by practitioners and security analysts. Consider a discussion about the development of

the converged IFRS and US GAAP accounting standard for revenue recognition.3 Paul Munter, a KPMG

audit partner, was quoted by the Bloomberg BNA Accounting Blog (November 26, 2013) at a Financial

Executives International financial reporting conference as stating, “From a U.S. perspective where the

instruments, leases, and revenues are issued by June 2011, [in order] to give companies sufficient time for quality

implementations, we believe that the mandatory adoption date for those three standards should be no later than January 1, 2015.” 2 James Schnurr became SEC chief accountant in October 2014 and said the SEC would revisit the question of US adoption of

IFRS (Global CPA Report, Nov. 19, 2014). Schnurr’s recent comments regarding a possible end of US GAAP and IFRS

convergence projects were made October 23, 2015 at an audit committee conference at the University of California, Irvine. 3 Both the FASB and the IASB approved the converged revenue recognition standard in December 2013; it was released May 28,

2014 and originally was to be effective beginning after December 15, 2016 for US public companies (Tysiac 2014). Tysiac

(2015) reported that the FASB received numerous inquiries regarding implementation of the revenue recognition standard. In

July 2015 the FASB formally delayed the effective date of the converged standard by a year (Tysiac 2015b).

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nuances are going to come into play…‘will we get away from industry specific literature?’” Under US

GAAP there are approximately 200 revenue recognition rules, and many reflect industry differences. As

Tysiac (2014, 41) notes, “U.S. companies are accustomed to prescriptive, industry-specific guidance.”

Operating, investing, and financing activities are reflected in firms’ financial statements, and

industry can be used as a proxy for differences in firms’ fundamentals. With the generally greater

incorporation of industry differences in accounting standards and their implementation under US GAAP,

it is plausible that differences in underlying fundamentals are more likely to be captured in US GAAP

financial statement numbers as compared to IFRS-based numbers. However, because US GAAP tends to

be more rule-driven than IFRS, managers under US GAAP can have an incentive to manage earnings,

including through transaction structuring, which would be expected to impair the quality of reported

earnings. On the other hand, to the extent that the principles-oriented IFRS regime affords managers

greater discretion over accounting policies and their implementation than US GAAP, managers of IFRS

firms can choose and implement accounting policies that better reflect underlying fundamentals, or they

can opt to manage earnings more. Hence, ex ante it is unclear which accounting regime, IFRS or US

GAAP, generally will yield earnings information that is more informative to investors. Moreover,

differences in country-level institutional features and their impact on reporting incentives arguably can

have a greater impact on earnings informativeness than differences in accounting standard regimes alone,

and must also be considered (Daske, Hail, Leuz, and Verdi 2008).

We can use the framework suggested in Dechow, Ge, and Schrand (2010) to structure our

discussion. Dechow et al. define reported earnings as unobservable fundamental performance (X) that is

converted into observable reported earnings by f, the accounting system. Dechow et al. (2010, 347-8)

argue that because “standard setters make trade-offs in setting standards across anticipated users’

needs…no individual decision-maker gets a representation of firm performance that is perfectly relevant

for his or her decision...[and no] single standard perfectly measures X for any given firm.”4 The question

4 Dechow et al. (2010, 348) give the example of cost of goods sold, which is a measure of a firm’s unobservable inventory

performance. Notwithstanding how GAAP defines COGS, “the resulting ‘standardized’ measure…will not be an equally good

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we are interested in is whether different f’s (IFRS vs. US GAAP) can make underlying fundamentals

significantly more distinct and apparent in reported earnings numbers. To the extent that industry is a

useful proxy for fundamental differences in firms’ economic activities and characteristics, we ask whether

earnings under IFRS vs. US GAAP better reflect firms’ underlying performance such that reported

earnings are more useful to investors, as captured by earnings response coefficients (ERCs). If so, then by

virtue of differences in underlying performance being more apparent under IFRS and/or US GAAP, the

result would be consistent with enhanced comparability of reported earnings numbers in that differences

between firms from different industries would become more distinct (FASB 2010; IASB 2010).

We focus on earnings informativeness between IFRS and US GAAP overall and with regard to

specific areas where the accounting policies differ between the two accounting regimes. We investigate

whether the informativeness of earnings (long-window ERCs) differs between US firms under US GAAP

and EU firms under IFRS for industries likely most affected by specific accounting policies. The

conceptual frameworks for both IFRS and US GAAP state that an objective of financial reporting is to

provide useful information about the reporting entity for decision-making purposes by current and

potential investors and other suppliers of capital. Dechow et al. (2010, 367) argue that ERCs, as a proxy

for earnings quality, provide direct evidence with regard to decision usefulness of annual earnings for

equity valuation (Liu and Thomas 2000), which motivates our focus on earnings informativeness.

We consider five specific accounting policies. The first is the accounting for multiple-element

revenue recognition of software (hereinafter software), which is an industry-specific accounting standard

under US GAAP that was absent under IFRS in our sample period. The other four accounting policies are

in areas where accounting standards are mandated under both IFRS and US GAAP and we expect the

given accounting policies will impact firms’ earnings in some industries differently than firms in other

industries. The four specific accounting areas are: accounting for R&D, inventory costing related to

LIFO, lessee accounting for leases, and goodwill accounting. As noted, we also investigate overall

measure of decision-relevant performance across all Xs (e.g., retail chains versus oil producing companies, to use the Graham and

Dodd example), and it will not be a perfect representation of any X.”

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earnings informativeness of IFRS vs. US GAAP, which notably differ in revenue recognition, asset

revaluation, fair values, and income statement presentation.

Our primary empirical analyses are based on a difference-in-differences method cross-sectionally

in the period following the EU’s mandated implementation of IFRS for EU firms relative to US GAAP

for US firms, and we match EU and US firms on size, industry, and year (following Barth et al. 2012) and

use partitioning variables based on separate country-level institutional factors (legal tradition, strength of

enforcement, and strengthening of enforcement). We focus on EU countries for several reasons: it

controls for the particular version of IFRS in use since the EU carved out a part of IFRS as mandated by

the IASB; most firms in EU countries adopted IFRS in 2005; and EU firms come from countries that are

similar in geographic location and operate in somewhat similar economic and regulatory environments. A

number of prior studies have focused on EU firms, including Li (2010) and DeFond, Hu, Hung, and Li

(2011). We identify industries ex ante that likely are substantially impacted by accounting policies in

specific areas and use EU and US firms in other industries that are unlikely to be substantially impacted

by the specific accounting policies as additional controls, and then compare the mean ERCs of IFRS firms

and US GAAP firms overall and for each of the five specific accounting areas.

Differences in mean ERCs associated with using IFRS vis-à-vis US GAAP would represent

evidence consistent with differences between the two regimes impacting the informativeness of earnings.

A lack of any differences in ERCs would be consistent with the choice of IFRS vs. US GAAP not having

meaningful effects on earnings informativeness.5 Hence, our research contributes to the debate over

whether differences between these two sets of high quality accounting policies – in particular, differences

that reflect the impact of specific accounting policies on industries likely most impacted by the given

accounting policies – are a matter of concern with regard to the use of IFRS by US firms. In addition, our

tests control for the impact of country-level institutional factors.

5 Dye and Sunder (2001) discuss arguments for and against allowing firms to present financial statements that are prepared on a

basis that follows either IFRS and US GAAP as opposed to requiring adherence to a single mandated set of standards. SEC chief

accountant J. Schnurr has said the SEC will consider the possibility of allowing US publicly traded firms to voluntarily include

supplemental IFRS financial statements in their financial reports (Tysiac, Dec. 8, 2014; Tysiac Dec. 9, 2015).

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Our empirical analyses compare earnings informativeness IFRS and US GAAP for the same time

period subsequent to the EU mandated implementation of IFRS,6 specifically, 2005-13. We also

subdivide the sample period into three sub-periods, 2005-07, 2008-09, and 2010-13 to assess the effects

structure our discussion using, respectively, during the initial period of mandated IFRS use, the financial

crisis, and the subsequent period of low growth on the empirical analyses. Our sample period choice and

test design have at least two advantages. First, the changes in accounting policies EU firms made under

IFRS are mandatory, so endogeneity should not be a major concern. Second, our focus on cross-sectional

comparisons means that we can minimize a concern raised by Christensen et al. (2013) who find that

there are other events that are concurrent with the mandatory adoption of IFRS in the EU, such that it can

be difficult to tease out the effect of changes in accounting standards from that of other changes.

Moreover, we also identify EU countries that strengthen their enforcement in conjunction with the

implementation of IFRS. Therefore, our empirical design and analyses mitigate concerns about

endogeneity of accounting choices and separability of concurrent events.

Our research thus contributes to the literature on IFRS vs. US GAAP by focusing on the impact,

if any, that industry-specific differences, which more often are reflected in US GAAP standards/guidance

than under IFRS, have on earnings informativeness. We also contribute to the comparability literature in

that by investigating whether accounting standards/guidance reflecting industry differences are associated

with differences in mean ERCs, we highlight the extent to which different accounting regimes make

underlying differences in fundamentals more apparent. The impact of such differences between

accounting regimes on earnings informativeness is presumably useful information regarding the SEC’s

decision of whether US firms should be subject to further convergence of US GAAP and IFRS. As a by-

product, our study may also shed light on the debate about IFRS vs. US GAAP by identifying a possible

reason – a differential impact on the informativeness of earnings associated with differential impacts of

accounting standards/guidance on industries and firms under the two accounting regimes – why

6 There is precedence for such an approach in the Barth et al. (2012) study in that some of their empirical tests compare US

GAAP and IFRS in the post-IFRS adoption period. (See their Tables 4, 6, 7, 8, and 9.)

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convergence of IFRS and US GAAP, let alone US adoption of IFRS, has proven difficult to achieve.7 In

addition, by subdividing our sample period we also provide evidence of the possible impact of

macroeconomic changes on earnings informativeness under the alternative accounting regimes.

We find over the full sample period (2005-13), which begins with the EU’s implementation of

IFRS, evidence of lower mean ERCs overall for EU firms under IFRS vis-à-vis US firms under US

GAAP. This is especially the case in the 2005-07 period, which is the early years of the EU’s adoption of

IFRS; also of interest is that we find no difference in overall earnings informativeness of IFRS and US

GAAP in 2010-13, which is after the financial crisis and is a period of relatively slow economic growth.

With regard to specific accounting areas, using our difference-in-differences approach, which focuses on

firms in industries likely most affected and uses firms that are likely least affected by given accounting

standards as additional controls, we find the following: (a) evidence that the use of IFRS is associated

with higher ERCs for firms in R&D intensive industries, except in the initial years of IFRS adoption; (b)

evidence of lower ERCs under IFRS for inventory costing for EU firms in LIFO-intensive industries that

can no longer use LIFO, except that during the financial crisis period US GAAP earnings for firms in

LIFO-intensive industries are less informative likely due to LIFO inventory depletions; and some

evidence of (c) more informative earnings software revenue recognition under IFRS and (d) less

informative earnings for lessees under IFRS. Moreover, our results are generally to country-level

institutional factors. The results suggest US GAAP’s more common recognition of industry differences in

the setting and implementation of accounting standards, in contrast to the general absence of that under

IFRS, yields more informative earnings overall. However, perhaps somewhat ironically, (i) the

differential treatment of qualified development costs in R&D-intensive industries under IFRS, in contrast

to US GAAPs lack of such differential treatment, is associated with more informative earnings, and

similarly, (ii) the evidence of greater earnings informativeness under US GAAP for firms in LIFO-

7 See Barth (2015) for a discussion of costs and benefits of global financial reporting.

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intensive industries during the post-financial crisis period is in the presence of US GAAP’s allowance of a

larger set of acceptable inventory costing methods, including LIFO.

II. Prior Research, Accounting Standard Differences, and Hypothesis Development

Prior Research8

Foster (1986, 197-199), building on earlier research, documents in a sample of US firms from a

large number of industries that changes in industry earnings (1964-1983) explain on average 36% of

changes in return on assets, compared to an average of 17% associated with changes in market earnings.

Biddle and Seow (1991) examine the relation between industry structure characteristics and annual

earnings informativeness for US firms. They find ERCs are related to industry barriers to entry, product

type, growth, financial leverage, and operating leverage, and argue that ERCs estimated by industry help

control for differences across industries in accounting method choices (Hagerman and Zmijewski, 1979).

Turning to cross-country research, an early study by Alford, Jones, Leftwich, and Zmijewski

(1993) finds that informativeness and/or timeliness of annual earnings based on domestic accounting

standards in 17 countries differ relative to US firms’ earnings under US GAAP. Barth, Landsman, and

Lang (2008) examine the question of whether International Accounting Standards (IAS), the predecessor

to IFRS, are associated with higher quality accounting information. They focus on voluntary IAS adopters

from 21 countries and compare accounting information of IAS adopting firms to non-adopting firms from

the same countries and find higher quality accounting amounts for the IAS adopters.

Landsman, Maydew, and Thornock (2012) assess the information content of earnings by

comparing stock return volatility and abnormal volume in three-day event windows surrounding earnings

announcements made before vs. after mandatory IFRS adoption for firms from 16 countries to firms from

11 countries (excluding the US) that maintained their domestic GAAPs. They find greater information

content for firms from the IFRS countries, with greater increases in countries with stronger enforcement.

8 See DeGeorge, Li, and Shivakumar (2015) for a comprehensive review of academic research on IFRS.

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Prior research has also investigated whether greater comparability of financial report information

is linked to IFRS adoption since improved comparability is a major benefit claimed for a single set of

global standards (Bolton 2008). DeFond et al. (2011) test and find that mandatory IFRS adoption is

associated with an increase in foreign mutual fund investment in firms from 14 EU countries due to an

increase in comparability proxied by the increase in accounting standard uniformity. Yip and Young

(2012) also investigate whether the EU mandatory IFRS adoption is related to improved comparability.

Building on concept statements from both the FASB (2010) and the IASB (2010), Yip and Young (2012,

1,768) focus on two facets of information comparability: “the similarity facet, which indicates whether

firms engaged in similar economic activities report similar accounting amounts, and the difference facet,

which indicates whether firms engaged in different economic activities report dissimilar accounting

amounts.” Using three proxies to gauge information comparability,9 they consider firms from 17 EU

countries and demonstrate that mandatory IFRS adoption is related to improved comparability across and

within countries with regard to the similarity facet. This is consistent with improved comparability being

driven by benefits from converging to a single set of standards, which reduces information processing

demands on financial statement users, and/or by higher quality information derived from the use of IFRS

vis-à-vis domestic GAAPs of the firms’ country of domicile. However, they find no evidence of a change

in the difference facet of comparability since firms from different industries do not look more different

under IFRS.10 DeGeorge et al. (2015) argue that by considering both similarity and difference facets, Yip

and Young’s analyses yield more nuanced results regarding comparability. Our examination of

9 Yip and Young (2012)’s three comparability proxies are: (i) similarity of accounting functions of pairs of firms in mapping

economic transactions to financial statements, where firms in the same industry (different industries) in different countries are

paired to test the similarity (difference) facet of comparability; (ii) degree of information transfer at earnings announcements

across similar (different) firms; and (iii) extent of information content of earnings and book value of equity based on an Ohlson

(1995) type model of year-end market value of equity regressed on annual earnings and year-end book value of equity. 10 Danos and Imhoff (1986, 31) argue, “…comparability is achieved when different companies use the same accounting practices

to report similar events. Information that meets the test of comparability will report like objects to be alike and will permit the

economic (or real) differences between unlike objects to be accounted for in such a way that users will understand the

differences.” A debate over the merits of accounting method diversity vs. uniformity has arisen several times over the years,

dating back at least to 1937 when the SEC began issuing Accounting Series Releases. In essence, the question is whether

managerial discretion over accounting choices allows firms the flexibility to use different accounting methods to better reflect the

special economic circumstances that the firms face or whether uniform methods facilitate comparisons across firms while

constraining self-serving managerial accounting choices. Yip and Young (2012, 1,768) quote from the IASB (2010, A36) that,

“…an overemphasis on uniformity may reduce comparability by making unlike things look alike.” Also see Barth (2015).

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accounting policies that impact specific industries and firms with differing underlying fundamentals,

allows us to shed further light on the difference facet of comparability.

Other research investigates and/or builds upon the extent of differences in specific accounting

standards between IFRS and country-specific GAAPs. An early study by Harris (1995) examined how

different shareholders’ equity would be in the financial statements of non-US firms if they used IAS or

US GAAP. He examines eight firms and focuses on each firm’s major accounting policies.

Ashbaugh and Pincus (2001) analyze voluntary IAS adoptions by a sample of firms from 13

countries. The study gauges the impact on security analysts’ earnings forecast accuracy of the number of

differences in accounting standards between IAS and the sample firms’ domestic GAAPs. They consider

12 accounting measurement or disclosure standards of which IAS adoption typically restricted the choice

of measurement methods and/or increased required disclosure. The findings indicate that analyst forecast

errors fall significantly (i.e., forecast accuracy increased) following firms’ IAS adoption and greater

reductions in analyst forecast errors are linked to greater reductions of differences between IAS and the

firms’ domestic GAAPs. The results are consistent with benefits from the convergence of standards

across countries and/or from higher quality information under IAS. Ding, Hope, Jeanjean, and Stolowy

(2007) quantify the differences between 30 country-specific GAAPs and IFRS. Their results suggest a

higher level of “absence” from IFRS (i.e., where a country’s GAAP does not have accounting policies

that are included in IFRS) is related to greater opportunities for earnings management and less firm-

specific information for investors.11 Aharony, Barniv, and Falk (2010) consider 14 European countries

and find that mandatory IFRS adoption is associated with greater value relevance of annual accounting

numbers in the year of IFRS adoption relative to the prior year with regard to accounting policies for

goodwill, R&D, and PP&E revaluation for which the authors expect considerable differences between

11 Yu (2011) studies the interaction of IFRS firms’ voluntary and mandatory disclosures surrounding the implementation of the

SEC’s 2007 rule that made reporting reconciliations of net income and shareholders’ equity of IFRS to US GAAP optional for

firms listed on US stock exchanges and using IFRS as issued by the IASB. Yu shows that IFRS firms increase their overall

voluntary disclosures, specifically, disclosures of prior reconciling items, in annual financial reports and earnings press releases

after the elimination of the reconciliation.

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IFRS and domestic GAAPs. There is also evidence of greater value relevance the greater the deviation of

IFRS values from the corresponding domestic GAAP-based values.

The above cited research primarily focuses on IFRS relative to non-US countries’ GAAPs. Leuz

(2003) finds insignificant differences in bid-ask spreads and share turnover of German firms that could

choose to report under either IAS or US GAAP while facing the same institutional environment. Gordon,

Jorgensen, and Linthicum (2010) consider 161 non-US firms from 25 countries that voluntarily list in the

US and report under IFRS and US GAAP for 2005-06, and find the firms’ US GAAP earnings generally

have greater relative value relevance in the presence of similar home country institutional factors (e.g.,

common law and high investor protection). Lin, Riccardi, and Wang (2012) examine high-tech German

firms that followed US GAAP but changed to IFRS in 2005 to investigate the impact of the change to

IFRS, and find lower earnings quality and lower value relevance post-IFRS adoption.

Barth, Landsman, Lang, and Williams (2012) demonstrate improved comparability of IFRS and

US GAAP accounting amounts after non-US firms adopt IFRS, although the value relevance and quality

of US firms’ accounting numbers generally remains higher than that of IFRS firms.12, 13 Barth et al. (2012,

90) conclude that “although widespread application of IFRS by non-US firms has improved financial

reporting comparability with US firms, significant differences remain.” Barth et al. explore potential

industry comparability differences by focusing on the three largest industries represented in their sample

(manufacturing, services, and finance, insurance and real estate) and find some evidence in the post-IFRS

adoption period of differences in comparability for those industry groups.

Joos and Leung (2013) conduct an event study of US firms to gauge stock market expectations of

the net costs or benefits of possible US IFRS adoption. They detect more significantly positive market

12 Barth et al. (2012) assess comparability of non-US and US firms pre- versus post-IFRS adoption and also in the post adoption

period. While employing several approaches, their analyses that are most relevant to our study assess comparability in terms of

adjusted R2s of regressions wherein 12-month stock returns are regressed primarily on earnings in the post-IFRS adoption period. 13 Armstrong, Barth, Jagolinzer, and Riedl (2010, p. 31) examine European stock market reactions surrounding events leading to

the EU’s IFRS adoption, and document an “incrementally positive reaction for firms [from countries] with lower quality pre-

adoption information [environments]…consistent with investors expecting net information quality benefits from IFRS adoption.”

There is an overall negative reaction associated with IFRS adoption events for firms in code law countries, perhaps reflecting

investor concerns over IFRS enforcement, whereas there is a more positive reaction for firms in countries with higher quality

information environments in the pre-adoption period, consistent with investors expecting net benefits from convergence to IFRS.

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reactions for US firms in industries for which IFRS is the predominant accounting regime followed by

firms in the respective industries worldwide, which is consistent with benefits of convergence to IFRS.

While Joos and Leung (2013) consider the differential impact on several industries with regard to IFRS

vs. US GAAP, the purpose of their research did not include a consideration of the impact of differences in

specific accounting standards/guidance on the industries, nor an assessment of earnings informativeness.

As noted, a number of studies discuss and compare accounting regimes with regard to countries’

institutional environments and argue the latter can dominate the accounting standard regime (Leuz 2003;

Ball, Robin, and Wu 2003; Daske et al. 2008; Aharony et al. 2010). However, Daske et al. (2008) note

there are persuasive arguments for and against capital market effects of mandatory IFRS reporting, and

thus it is an empirical question. They employ large samples of IFRS-mandating countries and from non-

IFRS countries and find modest evidence of capital market benefits associated with IFRS adoption. Daske

et al. (2008, 1152) conclude that “while it seems clear that the documented capital-market effects cannot

be attributed solely to the new reporting standards per se, it is an open question which other factors do

play a role…we suggest that our results likely reflect the joint effects of these institutional factors and the

IFRS mandate. Investigating this conjecture is an interesting avenue for future research.”

Based on prior research, an important unanswered question is: Does the absence of consideration

of industry fundamental differences in setting accounting standards/guidance impact earnings

informativeness with respect to IFRS vs. US GAAP, the two widely recognized high quality sets of

accounting standards, to country-level institutional factors? That is, do differences in the financial

accounting regimes of IFRS and US GAAP generate differences in reported earnings amounts in

industries likely most impacted by specific accounting standards that investors perceive or interpret

differently? Our study seeks to address this question and in so doing contribute to the literature and debate

over possible further IFRS and US GAAP convergence, let alone possible US GAAP adoption of IFRS,

and also to research on comparability by focusing on differences in earnings informativeness for firms in

industries most impacted by specific accounting standards.

IFRS and U.S. GAAP Accounting Standard Differences

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We initially reviewed the discussion of measurement and disclosure differences between IFRS

and U.S. GAAP as of late 2005, the first year the EU implemented IFRS and the start of our sample

period. A comparison prepared by PricewaterhouseCoopers (2006) identifies major accounting areas and

we focused on areas that differed substantially between the two accounting regimes. We also examined

additional sources for confirmation and to confirm that the differences existed over our sample period.14

In this section we briefly discuss the accounting areas we focus on.

Revenue recognition under multiple-element software contracts. First, we consider the industry-

specific accounting standard under US GAAP for multiple-element revenue recognition in the software

industry. Under US GAAP, arrangements related to software revenue recognition are divided into

separate units of accounting if such deliverables meet certain specified criteria. IFRS provides no detailed

guidelines for multiple-element software revenue transactions during our sample period so managers have

considerable discretion as to the accounting for such transactions. Hence, this is a clear-cut distinction,

with greater managerial discretion present under IFRS vis-à-vis US GAAP. However, IFRS No.15, the

new IFRS revenue recognition standard, states that the absence of any guidance for sales arrangements

with multiple elements had led some firms to supplement “the limited guidance in IFRS by selectively

applying US GAAP.” 15 If IFRS firms generally opt to follow US GAAP for the accounting for multiple

software-related deliverables, then we would not expect to observe differences in ERCs. On the other

hand, if managers of IFRS firms use their discretion to opt for some accounting treatment other than US

GAAP, then ERCs of IFRS firms could differ from those of US GAAP firms.

The remaining accounting areas we focus on are more common and are specifically mandated

accounting measurement and disclosure policies that can differ and/or for which there are differences in

guidance and thus possible differences in implementation under IFRS relative to US GAAP. Since

different industries have fundamentally different operating, investing, and financing activities, a given

14 The additional sources included Ernst & Young, (2005) “IFRS/US GAAP Comparison,” Deloitte (2007) “IFRSs and US

GAAP: A Pocket Comparison,” PwC’s “IFRS and US GAAP: similarities and differences” (2011), and McEwen (2009). 15 IFRS 15 Revenue from Contracts from Customers (May 2014), project summary and feedback statement (p. 3).

14

accounting standard or guidance can impact firms in certain industries differently from firms in other

industries, and this raises the possibility that for some firms’ their underlying fundamentals may not be as

faithfully reflected in reported earnings as the fundamentals of firms in other industries. In this category

we consider the accounting for R&D, inventory costing, lease accounting, and goodwill.

R&D. Under US GAAP virtually all R&D expenditures are immediately expensed, whereas

under IFRS while research costs are expensed development costs can be capitalized and amortized over

their useful lives when technical feasibility has been demonstrated along with the intention to complete

development and use or sell the resulting product. Thus, IFRS allows managerial discretion over the

treatment of some development costs, including the timing of their possible recognition as assets and

subsequent amortization (McEwen 2009).16 While managers can potentially use this discretion to manage

earnings through manipulation of real R&D activities or expensing (Bushee 1998), we expect IFRS is

more likely to better reflect underlying R&D activities and successes than US GAAP, and results in a

better matching of R&D expenses to revenues. Also, capitalization of development costs reveals the

portion of R&D expenditures that managers view as having future benefits and the expected useful lives

of such benefits. Based on both measurement and disclosure differences, we expect more informative

earnings under IFRS for firms in R&D-intensive industries.

Inventory costing and LIFO. Under IFRS, firms use FIFO or weighted average to determine

inventoriable costs. US GAAP also permits the use of LIFO. Hence, because more inventory costing

methods are generally accepted under US GAAP, there is more managerial discretion over inventory

costing. Assuming expected increases in inventory costs and non-decreasing inventory quantities, LIFO

approximates the matching of current costs to current revenues and yields a gross profit amount that better

measures operating performance than FIFO or average costing; i.e., LIFO better captures the extent to

which revenues exceed the cost of replacing goods that are sold.17 Under FIFO, inventory profits (i.e.,

16 Note that we exclude firms primarily engaged in marketable software development activities from our R&D analysis since

under US GAAP such firms can capitalize marketable software development costs. 17 Differences in inventory physical flows represent another fundamental operating difference across industries and conceptually

physical flows can be better reflected in earnings to the extent the inventory costing method is consistent with the physical flow.

15

realized inventory holding gains/losses) are recognized in earnings, and this yields a gross profit amount

that is a noisier measure of operating performance than gross profit under LIFO. Further, because

inventory balances under LIFO typically reflect out-of-date costs, the SEC requires firms using LIFO to

also disclose the current (or FIFO) cost of their beginning and ending inventories; the difference between

LIFO cost and the current cost of inventory is often referred to as the LIFO reserve, and changes in the

LIFO reserve reflect differences in cost of goods sold under LIFO vs. FIFO.

However, LIFO affords managers a greater opportunity to manage earnings than other methods

since inventory purchases can be timed to just before or after the end of an accounting period to impact

reported earnings in a desired way; and inventory depletions (i.e., LIFO liquidations) can be timed, which

typically results in higher earnings. Yet LIFO carries the requirement of book-tax conformity for US

firms using LIFO for tax purposes; this constrains managerial discretion due to the tension between

achieving tax benefits (i.e., avoiding inventory profits) and reporting higher earnings to shareholders.18

Hence, LIFO use has potential benefits and costs, but the prohibition of LIFO under IFRS

represents a major departure from its allowance under US GAAP. While LIFO generally yields a more

informative measure of earnings and US LIFO-using firms also disclose inventory on an as-if FIFO basis,

LIFO can afford greater earnings management opportunities. Hence, it can be unclear ex ante whether

managers under US GAAP will use their discretion over inventory costing to better reflect operating

performance or to manage earnings. However, our priors are that the more informative measure of gross

profit, along with the greater disclosure, under LIFO will generally have a more significant effect on

earnings than the greater opportunities LIFO firms have for earnings management, especially given that

the US book-tax conformity rule likely constrains earnings management and places a premium on firms

choosing LIFO to do so only if they generally expect inventory costs to rise and inventory reductions to

not occur. Hence, our expectation is for generally greater informativeness of earnings under US GAAP

18 US GAAP generally does not permit the reversal of previous inventory lower-of-cost-or-market write-downs. IFRS, however,

requires the reversal of such write-downs if there is a subsequent increase in the value of the inventory written-down. Note,

however, that lower-of-cost-or-market write-downs are uncommon for LIFO firms.

16

for firms in LIFO-intensive industries vs. under IFRS for FIFO or average costing firms in (formerly

permitted) LIFO-intensive industries.19

Lease accounting by lessees. Under IFRS, a finance (i.e., a capital) lease substantially transfers all

risks and rewards of ownership to the lessee. Substance rather than legal form is emphasized under IFRS;

while similar, US GAAP has “extensive form-driven requirements” (PwC 2006). For example, a lease

term covering a majority of a leased asset’s useful life is an indicator of a capital lease under IFRS. US

GAAP specifies capitalization if the lease term spans at least 75% of the asset’s useful life, but because

the notion of useful life is generally ambiguous, parties can structure lease contracts to avoid the lease

term limitation (McEwen 2009). Another example is reflected in the following as described by two US

partners of a Big 4 accounting firm in a seminar comparing IFRS and US GAAP. Under US GAAP, if the

present value of payments under a lease contract is less than 90% of the fair value of the leased asset, then

the lease is treated as an operating lease (assuming the lease does not otherwise have to be capitalized).

The accounting firm partners noted that under US GAAP if lease payments are, say, 89.99% of the fair

value, the lease is treated as failing to meet the 90% criterion and deemed to be an operating lease. Under

IFRS, however, the principle is that the lease will be capitalized if the present value of the lease payments

substantially equals the leased asset’s fair value. Hence, if the present value of the lease payments is

89.99% of the fair value of the leased asset, then it is likely the present value of lease payments would be

deemed under IFRS as substantially equaling the fair value of the asset and thus would be capitalized.20

Given the “bright line” lease rules approach under US GAAP, there can be greater incentives to

structure lease contracts to obtain operating lease treatment if mangers seek that for financial reporting

purposes. In contrast, the economic substance of lease transactions is more likely to be reflected under

IFRS than under US GAAP. Moreover, other things held constant, it is likely that if managers structure

lease transactions to avoid capitalizing leases under US GAAP, their firms will report higher cumulative

earnings over the earlier years of a lease term. However, other things are not the same in that operating

19 Krishnan et al. (2009) show that LIFO firms have better accruals quality than FIFO firms. 20 There also can be discretion in the determination discount rates irrespective of the accounting regime.

17

lease contracts are not the same as capital lease contracts. For instance, operating lease terms tend to be

shorter, which can mean a lower cost on the implied financing for operating leases. Further, given the off-

balance sheet aspect of operating leases, firms can be less likely to violate technical covenant provisions

on other debt. Thus, because capital and operating lease contracts are not identical, it is, ex ante, unclear

what to expect regarding mean ERCs for lease-intensive firms under IFRS vs. US GAAP.

Goodwill. Based on the ratio of goodwill-to-total assets, we identify industries that are most

active in growing through acquisitions. Under both IFRS and US GAAP goodwill is not amortized but is

instead reviewed for impairment (at least) annually. One difference between IFRS and US GAAP is that a

goodwill impairment charge is computed differently under the two regimes. For IFRS firms, it is the

difference between the recoverable amount of a cash generating unit, or CGU (the higher of its fair value

less selling costs or its value in use) and the CGU’s carrying value. Under US GAAP, there is a two-step

approach: (i) the estimated fair value of the reporting unit is compared to its carrying value, including

goodwill; then, (ii) if the carrying value exceeds the fair value of the reporting unit, the impairment

charge is the implied value of goodwill (derived from a hypothetical purchase price allocation) compared

to its carrying value.21 Another difference is that under US GAAP goodwill is assigned to an entity’s

reporting unit (i.e., to an operating segment) or to one level below, whereas under IFRS goodwill is

assigned to a CGU, which is the smallest set of identifiable assets generating cash flows that are largely

independent of other groups of assets within the firm. As a result, while it is somewhat more likely that

any goodwill impairment recognized under US GAAP will be allocated to a reporting unit that would be

one level below an operating segment, a CGU is likely to be no larger than a reporting unit, which

suggests that under IFRS goodwill impairments may be more closely tied to the net assets within a firm to

which the goodwill relates and thus it may be more likely that IFRS reflects a more informative indication

of the earnings effect of a goodwill impairment. To the extent that assessments of firm value are at least

21 In 2011 FASB added a “step zero” regarding goodwill impairments that permits more discretion. It is a qualitative test (e.g.,

based on macroeconomic, industry and market factors, overall financial performance, etc.) allowing management to skip the two-

step test if it is deemed more likely than not the reporting entity’s fair value is more than its carrying value (i.e., no impairment).

18

partially dependent on profitability of various segments or sub-units – for example, if analysts place

greater weight in valuation on the profitability of firms’ core segments (Chen and Zhang 2003) – then

differences between IFRS and US GAAP regarding goodwill impairments likely would result in segment

earnings being more informative for IFRS firms. Hence, there is the potential for more informative

earnings with regard to goodwill impairments under IFRS. Unfortunately, goodwill impairment data are

not available on Compustat Global for IFRS firms so we cannot focus on firms with goodwill

impairments. Hence, it is unclear ex ante whether firms in goodwill-intensive industries will have

differentially more informative earnings under IFRS or US GAAP.

Overall ERC. Our overall ERC analysis assesses the impact of accounting policy differences that

stem from a generally more principles-based approach (IFRS) vs. a generally more rules-based approach

(US GAAP). Several examples of differences are: There is less variation in revenue recognition practices

under IFRS compared to the considerable variation under US GAAP (during our sample period); while

both IFRS and US GAAP require revenues to be recognized at fair value, IFRS permits the use of

discounted present value as an estimate of present value in more situations, and revenue recognition is

generally more transparent by requiring the transfer of risk and rewards of ownership to, and control over

goods by, the purchaser (McEwen 2005). In general, there is greater use of fair value accounting under

IFRS. Also, reversals of asset impairment charges and the possibility of upward revaluations of certain

assets can occur under IFRS but not under US GAAP.22 There also are possible differences in income

statement presentation between IFRS and US GAAP; under IFRS expenses are shown by function

(production, distribution, selling or administrative) or by nature (salaries, taxes, etc.), whereas expenses

under US GAAP generally are shown by function (McEwen 2009). In addition, IFRS does not permit

extraordinary items. Ex ante, it is unclear whether managers use IFRS’s generally greater discretion to

better reflect underlying fundamentals or manage earnings and impair overall earnings informativeness.

Research Hypotheses

22 Upward revaluations of intangible assets and property, plant and equipment are permitted under IFRS, if certain criteria are

met, although these are rare in practice (Christensen and Nikolaev 2013).

19

The previous section focuses on differences between accounting measurement and disclosure

standards/guidance under IFRS and US GAAP, although US GAAP and IFRS are similar in a number of

areas and have similar conceptual frameworks. Thus, it is possible there are not significant differences in

earnings informativeness between the two accounting regimes. However, while Barth et al. (2012) find

improved comparability of US GAAP and IFRS after non-US firms adopt IFRS, the value relevance and

quality of accounting amounts was generally greater under US GAAP,23 and additional analyses by Barth

et al. suggest differences in comparability associated with the three largest industries represented in their

sample. Also, Lin, Riccardi, and Wang (2012) find lower earnings quality after IFRS adoption generally

for high-tech German firms that had previously used US GAAP.

Hence, it is unclear ex ante whether differences between IFRS and US GAAP accounting

standards/guidance overall are associated differences in earnings informativeness. Or, if they do differ: (a)

whether the generally greater discretion under IFRS overall and for multiple-element software revenue

recognition is associated with higher ERCs (reflecting manager use of accounting policies to better reflect

underlying fundamentals) or with lower ERCs (reflecting greater opportunities to manage earnings); (b)

whether the generally greater discretion under IFRS for R&D accounting is associated with higher ERCs

under IFRS (better reflecting underlying fundamentals); (c) whether the generally less discretion over

inventory costing under IFRS with respect to LIFO is associated with lower ERCs; and (d) whether

differences in lease and goodwill accounting yield more informative earnings under IFRS or US GAAP.

Formally, our first hypothesis is with regard to overall effects of IFRS and US GAAP differences.

Being primarily principles-based, IFRS are in general expected to give managers greater discretion over

accounting policies – choice among alternatives policies, if available, and implementation of accounting

policies – than US GAAP, which is generally more rules-based. Thus, we hypothesize (in alternative

form) as follows:

H1: If firms use the generally greater discretion present under IFRS to better reflect

23 See Barth et al. Table 6 in the EU post-implementation period. The difference is largely coming from code law and low

enforcement countries with a somewhat smaller difference from common law and high enforcement countries.

20

firm/industry underlying fundamentals (to manage earnings), then reported earnings overall will

be more (less) informative and firms will have higher (lower) mean ERCs relative to US GAAP.

Our remaining empirical analyses focus on firms in industries that are likely most highly

impacted by particular accounting standards/guidance since any impact of IFRS and US GAAP

differences should be most pronounced for such firms. The discussion above suggests the greater

discretion under IFRS vis-à-vis US GAAP with regard to capitalizing certain development costs is

expected to yield more informative earnings for firms in R&D-intensive industries under IFRS; the

generally greater discretion under US GAAP vis-à-vis IFRS regarding inventory costing is expected to

yield less informative earnings under IFRS for firms (formerly) in LIFO-intensive industries; and ex ante

it is unclear whether under IFRS the accounting for software revenue recognition, leases, and goodwill

yields more or less informative earnings for firms most likely impacted by accounting standards in each

of those areas. We formulate separate hypotheses for these three different cases, as follows:

H2: We expect greater earnings informativeness and thus higher mean ERCs for firms in R&D-

intensive industries under IFRS compared to US GAAP.

H3: We expect lower earnings informativeness and thus lower mean ERCs for firms in LIFO-

intensive industries that are prohibited from using LIFO under IFRS compared to US GAAP

under which LIFO is permitted

H4: If firms use the generally greater discretion present under IFRS to better reflect firm/industry

underlying fundamentals (to manage earnings), then reported earnings for the accounting for

software revenue recognition, leases, and goodwill will be more (less) informative and firms will

have higher (lower) mean ERCs relative to US.

Hence, H1 and H4 are non-directional hypotheses while H2 and H3 are directional hypotheses.

Empirical Design, Sample, and Data

Empirical Models

The following regression models the test of H1 regarding overall earnings informativeness

(before consideration of country-level institutional factors):

𝐴𝑅𝐸𝑇 = 𝑏0 + 𝑏1𝐸𝑈 × ∆𝑆𝑈𝐸 + 𝑏3∆𝑆𝑈𝐸 + 𝑏5𝑆𝑖𝑧𝑒 × ∆𝑆𝑈𝐸 + 𝑏6𝐵𝑀 × ∆𝑆𝑈𝐸 + 𝑏7𝐿𝑜𝑠𝑠× ∆𝑆𝑈𝐸 + 𝑏8𝑆𝑖𝑧𝑒 + 𝑏9𝐵𝑀 + 𝑏10𝐿𝑜𝑠𝑠 + 𝑏11𝑆𝑖𝑧𝑒 × 𝐸𝑈 + 𝑏12𝐵𝑀 × 𝐸𝑈+ 𝑏13𝐿𝑜𝑠𝑠 × 𝐸𝑈 + 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑓𝑖𝑥𝑒𝑑 𝑒𝑓𝑓𝑒𝑐𝑡 + 𝑦𝑒𝑎𝑟 𝑓𝑖𝑥𝑒𝑑 𝑒𝑓𝑓𝑒𝑐𝑡 + 𝜀 (1)

21

where ARET is the twelve-month market-adjusted buy-and-hold returns and the stock returns holding

period starts four months after the previous fiscal year-end. We measure a firm’s unexpected earnings,

∆SUE, by subtracting ex ante expected earnings from actual realized earnings per share (EPS) and scaling

by stock price. Expected earnings are proxied by mean analyst EPS forecasts from I/B/E/S at the

beginning of the fourth month after the previous fiscal year-end. BM is the book-to-market ratio and Size

is the market value of equity. Loss is an indicator variable that equals one if realized earnings are

negative. We interact BM, Size, and Loss with EU, i.e., whether a firm is domiciled in an EU country, to

allow the effect of firm characteristics to differ between EU and US firms. We also interact BM, Size, and

Loss with ∆SUE because prior studies show these firm characteristics are major determinants of ERCs

(Kothari 2001). Finally, we include country and year indicators24 and cluster standard errors by firm to

correct for potential time-series and cross-sectional dependence (Petersen 2009). Equation (1) models the

evaluation of whether overall earnings informativeness (i.e., mean ERC) is higher (lower) for EU, i.e.,

IFRS, firms than for US, i.e., US GAAP, firms. That is, consistent with H1, if mean ERC is higher

(lower) for IFRS firms, then we should observe that b1 in Equation (1) is significantly positive (negative).

Hence, the test of H1 is two-tailed.

For H2, H3, and H4 (regarding individual accounting policies), we have the following model

(before consideration of country-level institutional factors):

𝐴𝑅𝐸𝑇 = 𝑏0 + 𝑏1𝐼𝑁𝐷𝑥 × 𝐸𝑈 × ∆𝑆𝑈𝐸 + 𝑏3𝐸𝑈 × ∆𝑆𝑈𝐸 + 𝑏5∆𝑆𝑈𝐸 + 𝑏6𝐼𝑁𝐷𝑥 × ∆𝑆𝑈𝐸 + 𝑏7𝐵𝑀× ∆𝑆𝑈𝐸 + 𝑏8𝑆𝑖𝑧𝑒 × ∆𝑆𝑈𝐸 + 𝑏9𝐿𝑜𝑠𝑠 × ∆𝑆𝑈𝐸 + 𝑏10𝐼𝑁𝐷𝑥 × 𝐸𝑈 + 𝑏11𝑆𝑖𝑧𝑒 + 𝑏12𝐵𝑀+ 𝑏13𝐿𝑜𝑠𝑠 + 𝑏14𝐼𝑁𝐷𝑥 + 𝑏15𝑆𝑖𝑧𝑒 × 𝐸𝑈 + 𝑏16𝐵𝑀 × 𝐸𝑈 + 𝑏17𝐿𝑜𝑠𝑠 × 𝐸𝑈+ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑓𝑖𝑥𝑒𝑑 𝑒𝑓𝑓𝑒𝑐𝑡 + 𝑦𝑒𝑎𝑟 𝑓𝑖𝑥𝑒𝑑 𝑒𝑓𝑓𝑒𝑐𝑡+ 𝜀 (2)

where INDx is a 0/1 variable, which equals one to indicate that a firm belongs to an industry that ex ante is

likely to be more highly impacted by a given accounting standard, x. For example, for R&D accounting, if

a firm’s two-digit SIC belongs to the industries classified as likely being more highly impacted by the

R&D standard, then INDR&D equals one. Since we examine five specific accounting areas, we separately

24 Including country fixed effect dummies negates the need to have a separate EU main effects variable in the model since the

linear combination of all EU country fixed effects is EU.

22

estimate regressions for each, where x is R&D, LIFO inventory costing, software revenue recognition,

lease accounting for lessees, or goodwill accounting. (See Appendix A for industries identified ex ante as

likely more highly impacted by a given accounting policy.) All other control variables are defined

similarly as in Equation (1); see Appendix B for details.

First, note that b3, the coefficient on EU×∆SUE, captures the difference in mean ERCs between

IFRS and US GAAP firms. To the extent there are factors that drive cross-country differences in mean

ERCs, we use EU and US firms in industries that we expect to be minimally, if at all impacted by a given

accounting policy to absorb overall mean ERC differences. Second, the key feature of Equation (2) is our

use of a difference-in-differences method to assess effects of the impact of IFRS vs. US GAAP on firms

in industries that likely are impacted more by the accounting in a specific area. To do this, we use two

indicator variables, EU and INDx, and interact them with ∆SUE to capture the effects. Our focus is on b1,

the coefficient on EU×∆SUE×INDx, which captures effects of differences in an accounting area between

IFRS and GAAP on mean ERCs. Hence, for R&D (i.e., INDR&D=1), if earnings informativeness is higher

under IFRS vs US GAAP, then we should observe b1 to be significantly positive, consistent with H2. For

inventory costing with regard to LIFO, H3 predicts that b1 will be negative. Finally, for software revenue,

leases, and goodwill accounting, H4 is non-directional and we predict in each case that b1 will differ from

zero. Similar to Equation (1), we include country and year fixed effects and cluster standard errors by

firm when estimating Equation (2).25

While Equations (1) and (2) include country fixed effects, we enhance that control by also

incorporating country-level institutional factors. Specifically, we employ partitioning variables for a

country’s legal tradition, enforcement of shareholder rights, and changes in enforcement since prior

studies find these dimensions proxy for a variety of institutional features and are associated with cross-

sectional differences in various accounting quality and comparability indicators (Armstrong et al. 2010;

Barth et al. 2012; Yip and Young 2012). For instance, Armstrong et al. (2010) demonstrate the capital

25 Clustering by industry instead of by firm yields qualitatively similar results when estimating Equations (1) and (2).

23

market anticipates that IFRS adoption benefits will accrue to financial statement users in countries with

relatively poor information environments prior to the IFRS implementation in 2005, which tend to be

code law countries. Barth et al. (2012), in analyses that focus on the EU post-IFRS adoption period, find

that IFRS-based accounting amounts are generally comparable to US GAAP firms’ accounting amounts

with regard to value relevance as reflected in their returns analyses. Some of their analyses suggest this is

the case regardless whether IFRS firms are from common or code law countries or from high or low

enforcement countries in the post-IFRS adoption period.

Our key research question is whether cross-sectional differences in accounting

standards/guidelines per se are associated with predicted differences in mean ERCs in the EU post-IFRS

adoption period. It is possible that our empirical results could be impacted or even driven by differences

in country-level institutional factors rather than be due primarily to differences in the impact of

accounting standards/guidelines if such country-level factors are correlated with the industry partitions we

employ. Thus, we incorporate cross-country differences in institutional features into our analyses.

Sample and Data

Our initial sample comprises all European Union (EU) firms from Compustat Global file and all

US firms from the Compustat North America file. We obtain analysts’ earnings forecasts from I/B/E/S.

Stock returns data for EU and US firms are from Compustat Global and CRSP, respectively. Our sample

period is 2005-2013, and we also decompose our analyses into three sub-periods to take account of the

period from the initial EU implementation of mandatory IFRS (2005-07), the financial crisis (2008-09),

and the slow-growth period that followed (2010-13). The intersection of Compustat, I/B/E/S, and CRSP

yields a final sample of 25,258 firm-year observations and 5,157 firms; our largest reductions in firm-year

observations are the absence of IBES analyst data for firms or the lack of firms with data to yield

acceptable matched firms. Regarding the latter, we match each EU firm with a US firm based on year and

4-digit SIC, and then among those with matches we choose the one with the smallest size difference, so

the match is one to one with replacement.

24

In our analysis of overall ERC effects (H1) as well as in our tests of individual accounting

standards (H2, H3, and H4), we first divide the EU sample based on the country-level institutional factors,

and include a separate dummy for each country-level factor in the regression against US firms based on

Equation (1) for each subsample. For example, we replace EU×ΔSUE in Equation (1) with

Common×ΔSUE and Code×ΔSUE to test H1 in light of country-level legal tradition (Table 3, Panel A),

and, similarly, we replace INDx×EU×ΔSUE in Equation (2) with Common×INDx×ΔSUE and

Code×INDx×ΔSUE in testing H2, H3, and H4 with regard to legal tradition (Table 4).26 Note that

consideration of institutional factors provides additional controls and mitigates the concern that our

results are driven solely by institutional factors.

Table 1 summarizes the sample selection procedure. Except for stock returns, we winsorize all

continuous variables at the top and bottom one percent levels each year to avoid disproportionately

winsorizing observations in any particular years.

III. Results

Descriptive Statistics

We begin with a description of the sample. Table 2, Panel A displays the industry composition of

the sample based on a Fama-French 12-industry classification. Sample firms are drawn from many

industries, with the largest concentration from Business Equipment, Manufacturing, and Other. Panel B

shows the countries represented in the sample, their institutional features, and mean values for regression

variables for firms by country. IFRS firms are from 19 EU countries, and US GAAP firms are from the

US only and comprise 50 percent (by design) of the sample firm-years. Among IFRS firms, the highest

representation is from the UK, and also from Germany, France, and Sweden, and the distribution of

sample firms by country is similar to the results reported in Yip and Young (2012). The sample data on

26 Extending footnote 24, we have country dummy variables (US plus each EU country) and institutional factors dummies (for

example, common and code law), and if we were to put the US, country, and legal system dummies in the regression, one of

these variables would automatically/randomly be dropped in estimation by the statistical package (because any one is just a linear

combination of the other two). Hence, we do not need to include a US dummy when we include the pairs of country-level

institutional factor dummies; i.e., there are three different groups and just two sets of dummies are needed.

25

country-level institutional factors reveal that there is not much overlap of common law tradition, strong

enforcement, and the strengthening of enforcement associated with IFRS implementation. Hence, we

assess the effect of each factor in our regression analyses. Regarding characteristics of the IFRS and US

GAAP sample firm-years, the EU sample, on average, reflects a lower incidence of losses, higher book-

to-market ratios, and less negative ΔSUE over the sample period relative to the US firm-years. As noted,

we include interactions of each of the control variables with ∆SUE in the regressions.27 Panel C reports

the pairwise correlations among variables. Abnormal stock returns are positively correlated with ∆SUE

and BM, and negatively associated with Loss.

Benchmark Results

Table 3 reports results for the test of overall informativeness of IFRS vs. US GAAP (H1), over

the entire sample period and for the three sub-periods, and includes partition variables for the country-

level institutional factors. Panel A presents results for country-level legal tradition, common vs. code law

(La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1998), Panel B shows results for weak vs. strong

country-level enforcement (La Porta et al. 1998), and Panel C for no changes vs. changes in country-level

legal enforcement (Christensen et al. 2013).28 In each panel column (1) displays results for the entire

sample period (2005-13), column (2) shows results for the pre-financial crisis period (2005-07), column

(3) displays results for the period of the financial crisis (2008-09), and column (4) presents results for the

period 2010-13, a period of slow growth.

As a benchmark, untabulated results of Equation (1) without the country-level partitioning

variables indicate that over the entire sample period the coefficient estimate of b1 on EU×∆SUE is -0.76,

which is significant (0.10 level, two-tailed test). This represents some evidence that the overall mean ERC

27 We match on Size (as well as industry and year) and EU and US firm-years on average do not reflect a significant difference in

Size. Still, we include Size in our regressions since cross-sectionally Size can differ among firms in different pairs in specific

analyses, and Size also is a cross-sectional determinant of ERCs. 28 Recall, EU×ΔSUE in Equation (1) becomes Common×ΔSUE and Code×ΔSUE in testing H1 (e.g., Table 3, Panel A). Since US

firms are coded as zero for either common or code dummy, in effect ΔSUE reflects the baseline ERC for US firms, while

Common×ΔSUE and Code×ΔSUE reflect effects. Similarly, INDx×EU×ΔSUE in Equation (2) becomes Common×INDx×ΔSUE

and Code×INDx×ΔSUE in testing H2, H3, and H4 (e.g., Table 4). We have three groups (EU-Common firms, EU-Code firms,

and US firms), and US firms are baseline firms used to compare to EU-Common or to EU-Code firms.

26

for EU firms under IFRS is lower than that for US firms under US GAAP. In terms of magnitude, this

suggests the mean ERC for EU firms is 18.5% lower than the mean ERC for US firms (4.30) given the

same level of unexpected earnings over the entire sample period. Untabulated results for the initial period

under IFRS (2005-07) indicate a coefficient (b1) of -0.81 (p-value = 0.09). For the fiscal crisis period

(2008-09), b1 is -1.11 (p-value = 0.12), while for the years 2010-13 b1 is -0.00. The latter result suggests

that in the slow growth period that followed the fiscal crisis, overall mean ERCs of IFRS and US GAAP

firms were essentially the same.

With regard to the control variables, the untabulated benchmark results indicate the coefficient on

∆SUE, which reflects the ERC for US GAAP firms, is positive and statistically significant overall (4.30)

and for each sub-period. Interestingly, US GAAP firms’ overall mean ERCs decline over the sample

period: 6.53 for 2005-07; 5.76 for 2008-09; and 2.49 for 2010-13. Smaller firms and profitable firms earn

higher returns over all sub-periods, and value firms do so especially in the 2008-09 sub-period but not in

2010-13. In addition, interaction terms between firm characteristics and unexpected earnings

(Loss×∆SUE, Size×∆SUE, and BM×∆SUE) indicate negative statistical significance for less profitable

firms overall, implying lower mean ERCs. Further, larger firms and firms with better growth

opportunities have higher ERCs during 2010-13. These results are consistent with prior findings (Hayn

1995; Collins and Kothari 1989). Over the entire sample period the benchmark model explains about 18

percent of variation in annualized abnormal stock returns.

Main Results – Overall Informativeness

Our first main results are presented in Table 3 in which Equation (1) is modified by including

country-level institutional partitioning variables. The findings in Panel A indicate a negative overall mean

ERC for both common law countries and code law countries for the full sample period; the coefficient

estimates on Common×∆SUE and Code×∆SUE, are -0.45 and -0.85, respectively, with the code law

result being significant (p-value = 0.06, two-tail). This indicates that for the code law-∆SUE interaction

coefficient, the mean ERC is 20.7% lower (0.85/4.10) per unit of ∆SUE. Further, ∆SUE has a sample

standard deviation of 0.087 (untabulated); hence, for a one standard deviation change, the stock return is

27

7.4% (0.087*0.85) lower for code law firms. Also note that a test of differences of the common and code

law interaction coefficients (b1 - b2) is insignificant. With regard to the sub-period results, the code law

coefficient is negative and significant (-1.20, p-value = 0.09) for the 2008-09 sub-period, although again

the common and code law coefficients are not significantly different for this or the other sub-periods

With respect to strength of enforcement, Table 3, Panel B shows that full sample period ERCs are

significantly negative (-0.79) in weak enforcement countries (p-value = 0.08), whereas they are negative

but insignificant for strong enforcement countries (-0.55), although the difference in coefficients is not

significant. Table 3, Panel C presents results for countries that did not change enforcement vs. those that

strengthened legal enforcement with the EU adoption of IFRS. Over the entire sample period IFRS firms

both from EU countries that changed legal enforcement and EU countries that did not change have lower

ERCs (-0.87 for no change countries, p-value = 0.06, and -0.66 for change countries, p-value = 0.18), and

the difference in coefficients is not significant. These results appear to be driven primarily by the 2005-07

sub-period for change firms, and by the 2008-09 sub-period for the no change firms, where there is a

weakly significant (p-value = 0.11) difference in coefficients between the change and no-change firms.

In sum, there is evidence that overall earnings informativeness of EU firms is lower than that of

US firms (H1). This appears primarily due to EU firms from countries following code law and having

weak enforcement and no strengthening of enforcement. There is evidence of negative mean ERCs for

firms from EU countries following common law and those with strong enforcement and strengthened

enforcement, but those results are not significant; also, tests of differences in coefficients between

country-level institutional factors generally are not significant.

Main Results – Specific Accounting Area Differences

We turn next to the analyses of whether the informativeness of earnings differs in predictable

ways between IFRS and US GAAP regarding R&D (H2, one-tail positive) and LIFO (H3, one-tail

negative), or simply differs for software, leases, and goodwill accounting (H4, two-tail non-directional)

between US firms under US GAAP and EU firms under IFRS. For each of the five accounting areas we

consider, we first classify industries as those that likely are impacted the most by the given accounting

28

policy, and then estimate separate regressions based on Equation (2) and including country-level

institutional partitioning variables. The regressions reflect a difference-in-differences approach. We

determine whether an industry is likely highly impacted by a given accounting policy based on prior

research or, if we cannot find an appropriate classification scheme from prior studies, we create our own

classification. (See Appendix A.)

The analyses of specific accounting standards include country-level partitions for legal tradition

(Table 4), strength of enforcement (Table 5) and strengthening of enforcement (Table 6). In each table,

Panel A presents results for H2 regarding R&D accounting, Panel B presents results for H3 regarding

LIFO, and Panels C, D, and E, respectively, results for software revenue recognition, leasing, and

goodwill accounting. Focusing initially on Table 4, Panel A, the results for R&D reveal positive

coefficients on Common×IND×∆SUE over the full sample period for firms from both EU common and

code law countries (respectively, 1.15 with p-value = 0.06, and 0.76 with p-value = 0.13); the difference

in coefficients is not significant. This suggests that, as predicted, IFRS firms in R&D-intensive industries

have higher mean ERCs than US GAAP firms in R&D intensive industries. This is consistent with the

IFRS accounting policy for R&D being impactful beyond countries’ legal traditions, and thus with R&D

accounting under IFRS, which reflects greater discretion given to managers than under US GAAP,

yielding more informative earnings, consistent with H2.29 With regard to sub-periods, the R&D results for

both common and code law countries indicate significant and positive coefficients in the financial crisis

period (2008-09), but a strongly negative b1 coefficient for 2005-07.30 This latter result is unexpected and

raises a possibility that during the early years of the EU mandated adoption of IFRS, firms and investors

were not accustomed to interpreting the impact of the capitalization of certain development costs on the

informativeness of earnings. The sub-period results for code law countries indicate the same directional

29 One might conjecture that while R&D expenditures are expensed in the US, the disclosure of total R&D expenditures could in

principle enable investors to estimate US firms’ R&D costs that could be viewed as capitalized development costs (Lev and

Sougiannis 1996) and thus result in no differences in R&D firms’ ERCs under IFRS and US GAAP. The results are not

consistent with this. Perhaps the fact that under IFRS firms capitalizing R&D are effectively also disclosing the amount of

development costs qualifying for capitalization and also the expected useful life of the capitalized development costs. 30 The 2005-07 result is negative and would be significant if we had hypothesized a non-directional effect for R&D.

29

(but not significant) effect and the results comparing common and code law country coefficients are

significantly different only for the 2005-07 sub-period.

Table 4, Panel B presents the results for LIFO and country legal tradition. Over the full sample

period there are positive but insignificant coefficients for both common and code law countries (1.10 and

0.33, respectively). Across all sub-periods, the common and code law results for LIFO are not

significantly different. Interestingly, the results during the 2008-09 sub-period are opposite to what is

predicted by H2: there are positive coefficients for firms from both common and code law countries (2.59

vs. 1.06), and the mean ERC for common law countries would be significant if H2 was non-directional.

Positive ERCs for IFRS firms during the financial crisis period is consistent with FIFO (and average

costing) making EU firms’ gross profit amounts somewhat more informative relative to LIFO-based gross

profit amounts at a time when US firms using LIFO were experiencing inventory liquidations and thus

reporting higher gross profit in the midst of a deep recession.31 In contrast, for the 2010-13 period, ERCs

are negative (-1.09 vs. -1.03) for both common and code law countries, and significant for code law

countries (p-value = 0.04). During this slow growth period, investors appear to value the use of LIFO

more highly than FIFO, likely because LIFO provides a less noisy measure of gross profit by

approximating the matching of current costs of inventory to current revenues, which can enhance attempts

by financial statement users to assess current operating performance and predict future performance.

Thus, the results are consistent with H2 for US vs. code law EU countries only in the 2010-13 sub-period.

Panels C, D, and E of Table 4 display results for H4. Regarding software, Panel C indicates

positive coefficients for both code and common law countries over the full sample period, and for the

2005-07 and 2008-09 sub-periods, with the results for code law countries significant overall (p-value =

0.07, two-tail) and for the 2008-09 sub-period (p-value = 0.01). As previously noted, the absence of an

industry specific standard/guidance under IFRS for software raises the possibility that at least some EU

31 We analyzed changes in the LIFO reserve for US LIFO firms. For firms with non-zero and non-missing LIFO reserves, the

LIFO reserve decreased 6.88% (t = 2.34) in the financial crisis period from the pre-crisis period. As a comparison, there was an

increase of about 4.2% (t = 2.44) over the pre-crisis period. The effects are similar but smaller if we scale by total assets.

30

firms opted to follow US GAAP rather than some other approach. If that was more likely for common law

firms, then one interpretation of the positive ERCs for firms from code law countries is that code law EU

firms following differing country approaches for software on average generate more informative earnings.

With regard to lease accounting (Table 4, Panel D), there are generally negative coefficients for

common and code law firms across the sub-periods (except 2008-09 for common law firms), with

negative and significant effects for firms from code law countries in 2005-07 and significant effects for

common law firms in 2010-13. Significant differences in mean ERCs between common and code law

countries occur in the 2005-07 and 2008-09 sub-periods when code law firms have less informative

earnings. The results are somewhat consistent with H4 and suggest that the more principles-based

orientation under IFRS for lease accounting is associated with somewhat less informative earnings; this

may reflect greater opportunities for earnings management with regard to lease transactions under IFRS in

the 2005-07 and 2008-09 sub-periods. However, less informative earnings in common law countries is

weakly indicated for 2010-13 (p-value = 0.16, two-tail). Finally, Panel E (Table 4) presents results for

goodwill accounting and reveals insignificant coefficients overall, and for each sub-period, and no

differences between common and code law countries.

Table 5 displays results when considering country-level strength of enforcement. In Panel A, the

R&D results for the full sample period indicate higher ERCs for firms from high enforcement (0.77, p-

value = 0.14) and low enforcement (0.95, p-value = 0.08) countries, and no significant difference between

the high and low enforcement firms. For the 2005-07 sub-period, high enforcement firms have lower

ERCs that would be significant if H2 was non-directional, and the high enforcement firms have

significantly lower ERCs than the low enforcement firms (-1.62 vs. -0.04, p-value = 0.05). Low

enforcement firms have significantly positive ERCs during the financial crisis period.

The results for LIFO-intensive firms with regard to strength of enforcement are in Table 5, Panel

B. Contrary to expectations, high enforcement firms have significantly larger ERCs than low enforcement

firms (p-value = 0.04) over the full sample period, and similar to the results in Table 4, Panel D, this is

driven by the results in the financial crisis period (p-value = 0.00). EU firms from both high and low

31

enforcement countries have significantly lower ERCs during the 2010-13 period, again similar to the

Table 4 results for common and code law firms. Regarding software accounting, the results do not

indicate differences between high and low enforcement firms, and similar to Table 4, Panel C reveal

positive coefficients for both high and low enforcement firms during the financial crisis. With regard to

leases, there is evidence that high enforcement firms have larger ERCs than low enforcement firms for

2005-07 and 2008-09; in both of those periods, there are negative coefficients for the low enforcement

firms (-2.79, p-value = 0.00, and -0.81, p-value=0.63, respectively). Both high and low enforcement firms

have negative ERC effects in the 2001-13 period, with the results for high enforcement firms significant

(p-value = 0.08). The results for goodwill accounting do not indicate any significant effects.

Lastly, we consider the results in Table 6 for firms that strengthened their enforcement vs. those

that did not change their enforcement policies. The results for R&D accounting in Panel A are similar to

the results in Tables 4 and 5. There is some suggestion of greater ERCs over the full period for both

enforcement change and no change firms, especially during the 2008-09 sub-period, and it is also the case

that there again is an unexpected negative coefficient for change firms during 2005-07. Regarding LIFO,

again consistent with Tables 4 and 5, both change and no-change firms have lower ERCs during the 2001-

13 period, as predicted by H3. There is evidence in Table 6, Panel B of the unexpected positive

coefficient during the financial crisis period for non-change firms. The software accounting results in

Panel C are consistent with the results in Table 4 and especially Table 5, and suggest that both change and

no change firms have positive and significant coefficients during the financial crisis, and no evidence of

differences between change and no change firms overall and across all sub-periods. Panel D shows the

lease results, and consistent with Tables 4 and 5, no change firms have significantly lower ERCs than

change firms in both the 2005-07 and 2008-09 sub-periods. Goodwill results again are not significant.

To summarize, estimating ERCs separately for country-level institutional factors for specific

accounting policies, yields the following results. For R&D accounting, there is evidence for the full

sample period and the financial crisis period of higher ERCs under IFRS, suggesting more informative

earnings, supportive of H2. Moreover, that there are no systematic ERC differences regarding R&D

32

accounting between firms from common vs. code law countries, from high vs. low enforcement, and for

enforcement change vs. no change firms in the full sample period. However, there are unexpectedly lower

ERCs in the 2005-07 period, which may reflect the initial transition to R&D accounting under IFRS by

firms and/or investors.

With regard to LIFO, there is little evidence of differences between common vs. code law firms

and change and no change firms, although there is some indication of greater ERCs when considering

high vs. low strength of enforcement firms. Also, the expected negative coefficient under H3 is

consistently detected for LIFO in the 2010-13 period regardless of which country-level institutional factor

is considered. We also observe unexpectedly lower ERCs for LIFO use during the financial crisis sub-

period, when considering common law firms and high enforcement firms, and also no change firms.

Hence, US GAAP earnings reflecting the use of LIFO are more informative in the slow growth post-

financial crisis period vis-a-vis FIFO (or average costing), but less informative during the financial crisis.

Regarding software, over the full sample period there is some evidence of higher ERCs under

IFRS for code law and no change firms. However, during the 2008-09 sub-period there is consistent

evidence of higher ERCs for software accounting under IFRS irrespective of country-level legal tradition,

strength of enforcement, or enforcement change. These results are somewhat supportive of H4.

Lease accounting results indicate lower ERCs under IFRS, but in the 2005-07 sub-period it is for

code law, low enforcement; and no change firms, while for the 2010-13 sub-period it is for common law,

high enforcement, and change firms. Hence, there is some evidence that lease accounting under IFRS

yields less informative earnings than under US GAAP, somewhat consistent with H4. There is also

evidence of differences between each pair of institutional factors, which generally suggests lower ERCs

for code law, low enforcement, and no change firms. Finally, there is no evidence of significant ERCs or

of ERC differences regarding institutional factors for goodwill accounting under IFRS vs. US GAAP.

This is based on our sample of firms from industries that tend to be active in the take-over market.

Discussion

33

It is useful to recall the findings in Barth et al. (2012): (a) IFRS firms’ accounting amounts

generally are more comparable to US GAAP firms’ accounting amounts with regard to value relevance

after EU firms switch from their domestic GAAPs to IFRS; (b) accounting quality of US GAAP firms’

accounting amounts generally remain higher than IFRS firms’ accounting amounts after EU firms

implement IFRS; and (c) focusing on the post-IFRS adoption period, there generally are similar results

when partitioning based on country-level legal tradition and enforcement.32 As noted, our analyses are of

IFRS vs. US GAAP firms in the EU post-IFRS adoption period and we use of country-level institutional

partitioning variables in the regression analyses. In Table 3 we find evidence of differences in the

subsamples for IFRS relative to US GAAP firms overall, which suggests generally overall earnings

informativeness is lower for IFRS relative to US GAAP firms. The results from Tables 4, 5, and 6

indicate significant effects for R&D overall, suggesting higher ERCs for R&D accounting under IFRS,

and suggesting greater earnings informativeness for firms in LIFO-intensive industries under US GAAP,

although there is sensitivity in the results to differing macro-economic environments. There is also some

evidence of higher ERCs for software and lower ERCs for lease accounting under IFRS. Hence, the

findings suggest that, notwithstanding increased comparability of IFRS and US GAAP accounting

amounts, earnings informativeness under IFRS generally differs from that of US GAAP firms overall

(H1) and for the accounting for R&D (H2), LIFO (H3), and somewhat for software and lease accounting

(H4), and there is evidence that some findings are to country-level institutional factors.

Also, note that Table 4, Panels A-E indicate that the coefficients on control variables

Common*ΔSUE and on Code*ΔSUE (i.e., b3 and b4, respectively), which together replace EU*ΔSUE

Equation (2) and capture overall differences in mean ERCs between IFRS and US GAAP firms, are

generally negative and often significant for code law firms but also for common law firms, except in the

2010-13 sub-period. Tables 5 and 6 reveal similar results. Hence, these results reflect the generally lower

overall mean ERCs under IFRS. As previously noted, to the extent there are factors that drive cross-

32 Barth et al. (2012) find that results for firms from common law countries are mixed with regard to accounting quality (i.e.,

earnings smoothing, accrual quality, and timeliness).

34

country differences in mean ERCs, we use EU and US firms in industries that we expect are not

substantially impacted by a given accounting policy to absorb overall mean ERC differences, and we

estimate models in which we partition by country-level institutional factors to allow coefficients to be

different, which results in a better control. This provides considerable confidence that the differences we

observe are driven at least in part by accounting standard/guidance differences between IFRS and US

GAAP rather than solely by country-level institutional factors. That is, it is unlikely that differences in

institutional features between EU firms and US firms are the sole drivers of our results. Hence, our

conclusion is consistent with that of Daske et al. (2008) in their study of the capital market effects of the

mandatory adoption of IFRS: we find that accounting standards and institutional factors both play a role

in understanding capital market effects associated with earnings.

IV Summary and Conclusions

We assess the informativeness of earnings of EU firms relative to US firms overall and in five

specific accounting areas that differ between IFRS and US GAAP. Both accounting regimes are viewed

as high quality systems, but they differ in that IFRS is predominantly a principles-based system in

contrast to US GAAP, which has rules-based accounting standards/guidelines. One manifestation of that

difference is that there is generally more discretion under IFRS relative to US GAAP, and this potentially

better enables managers of IFRS firms to report earnings that best reflect their firms’ underlying

fundamentals or to exploit more opportunities to manage earnings. Another difference is that US GAAP

is more likely to reflect fundamental differences across industries whereas that is generally absent under

IFRS. We investigate whether earnings informativeness as reflected in mean ERCs differs in

hypothesized ways between EU firms under IFRS and US firms under US GAAP overall and in industries

likely most impacted by specific accounting policies. In so doing we also further research on

comparability of earnings numbers in that we explore whether differences across firms in different

industries and more apparent under IFRS vs. US GAAP.

We examine ERCs cross-sectionally in the EU post-IFRS mandated implementation period and

find lower overall mean ERCs for IFRS firms relative to US firms. Using a difference-in differences

35

approach, wherein we identify ex ante industries that likely are most affected by specific accounting

standards/guidance and include country-level institutional factors as partitions, we gauge the

informativeness of earnings for EU firms using IFRS in those industries relative to US firms under US

GAAP in the same industries. Our controls include EU and US firms that are in industries that likely are

not impacted substantially by the specific accounting areas that we examine. We find (a) generally higher

ERCs for IFRS firms in R&D-intensive industries, which is somewhat ironic in that R&D accounting

under IFRS is more reflective of industry differences than under US GAAP since firms in R&D-intensive

industries, which are most affected by R&D accounting standards, are permitted to capitalize qualified

development costs under IFRS. That is, IFRS allows for the possibility that R&D activities lead to assets

being created and recognized in the financial statements. We also find (b) ERCs are lower under IFRS for

EU firms in LIFO-intensive industries that can no longer use LIFO, except that during the financial crisis

period IFRS firms, which use FIFO or average costing, have higher ERCs than US firms using LIFO,

likely due to LIFO liquidations. US GAAP permits greater discretion in the choice of inventory costing

methods in that LIFO is permitted. We also find some evidence of (c) more informative multiple-element

software revenue recognition under IFRS, and (d) less informative earnings for lessees under IFRS.

Moreover, there is considerable evidence that our results are not driven by country-level institutional

factors but rather reflect both the accounting standards and the institutional factors. The results suggest

that recognition of industry differences in setting and implementing accounting standards can yield more

informative earnings that reflect underlying fundamental differences.

Future research might investigate the extent to which firms and/or their industry associations

lobby the FASB or the SEC in support of, or in opposition to, increased convergence of US GAAP and

IFRS. That is, do firms or their industry associations that are most impacted by R&D accounting (or LIFO

inventory costing) lobby for (against) IFRS since it likely would result in such firms reporting less

informative earnings?

36

Appendix A: Industries Likely Most Impacted by Accounting Standards

Accounting Standard Industries Likely to be Most Affected by a Given Accounting

Standard

R&D = 2-digit SIC 28, 35, 36, 37, 38 in Lev and Sougiannis

(1996).33

Revenue recognition under

multiple-element software

contracts

= 4-digit SIC 7370, 7371, 7372, 7373, 7374 in Zhang (2005).

LIFO Inventory Costing =

We calculate the percentage of firms using LIFO as a primary

inventory costing method for each 2-digit SIC; we then select

the top one-third of industries based on the percentage of

firms using LIFO in each 2-digit SIC industry, and designate

such industries as LIFO intensive industries. 2-digit SICs

include 21, 22, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 39, 46,

50, 51, 52, 53, 54, 55, 59, 75.

Lease Accounting =

Two industries, transportation and retail, are classified as

lease intensive industries based on the Fama-French 48

industry classification in Henderson and O’Brien (2013).

Goodwill =

We calculate the median goodwill-to-total asset ratio for each

2-digit SIC; we then select the top one-third of such industries

based on the industry goodwill-to-total assets ratios, and

designate such industries as goodwill intensive industries. 2-

digit SICs include 7, 16, 17, 21, 25, 26, 27, 30, 34, 37, 41, 47,

50, 54, 55, 64, 72, 75, 76, 80, 82, 89.

33 Software development firms have been dropped from the R&D analysis since under US GAAP firms have the ability to

capitalize software development costs.

37

Appendix B: Variable Definitions

Variable Name

Definition

∆SUE =

Unexpected earnings per share equals actual realized earnings

minus expected earnings, which is proxied by mean analyst

earnings forecast from I/B/E/S at the beginning of the fourth

month after the previous fiscal year-end, and scaled by the

firm’s stock price at the fourth month after the previous year

fiscal year end;

Size =

Firm size, measured as the natural log of the market

capitalization (in millions) at the beginning of the fourth

month after the previous fiscal year-end;

BM =

Book-to-market ratio, calculated as the book value of equity

divided by the market value of equity at the end of the most

recent fiscal year for which the data are available;

Loss = Indicator variable that equals one if the I/B/E/S realized

earnings is negative, and zero otherwise;

ARET =

Market-adjusted stock returns, calculated as a firm’s 12

month buy-and-hold stock return measured starting after the

fourth month of the previous year fiscal year end minus

market-adjusted returns;

EU = Indicator variable that equals one if a firm is domiciled in an

EU country;

INDx = Indicator variable that equals one if a firm belongs to an

industry classified as most affected by a given accounting

standard x.

Our inclusion of country fixed effect dummies negates the need to have a separate EU main effects variable in the regression

models. That is, the linear combination of all EU country fixed effect is EU, so EU dummy is excluded. For Size, all local

currencies are converted to US dollars. The remaining variables are either ratios or indicators.

38

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41

Table 1 Sample Selection

Number of firm-year observations from Compustat with stock returns

over the period 2005-13

64,281

Exclusion:

missing analyst forecast

19,788

firm-country with fewer than 10 observations 6

firms missing necessary financial data 6

firms without matching firms on industry, size, year* 19,187

Total number of firm-year observations in the initial sample 25,258

* We match each EU firm with a US firm based on year and 4-digit SIC. Then among those with matches (year, industry, and

size), we choose the one with the smallest size difference, so the match is one to one with replacement. (Our regressions also

control for country-level institutional factors.)

Table 2 Panel A Sample Composition by Industry

Industry Frequency Percent

Consumer Non-Durables 1,950 7.72%

Consumer Durables 718 2.84%

Manufacturing 3,954 15.65%

Energy, Oil, and Gas 834 3.30%

Chemicals 898 3.56%

Business Equipment 4,824 19.10%

Telecommunication 1,006 3.98%

Utilities 694 2.75%

Retail 2,726 10.79%

Healthcare 2,228 8.82%

Finance 66 0.26%

Other 5,360 21.22%

Total 25,258 100.00%

42

Panel B Descriptive Statistics

∆SUE Size BM Loss ARET

Country N Com Enf Chg Mean Mean Mean Mean Mean

Australia 163 0 0 0 -0.01 7.29 0.61 0.10 0.13

Belgium 337 0 0 0 -0.01 6.58 0.75 0.20 0.05

Czech 26 0 0 0 -0.01 8.60 0.64 0.12 0.05

Germany 1593 1 0 1 -0.03 6.39 0.71 0.19 0.06

Denmark 361 0 0 0 -0.02 6.59 0.60 0.25 0.03

Spain 505 0 0 0 -0.03 7.41 0.63 0.19 0.06

Estonia 31 0 0 0 -0.01 5.47 1.73 0.19 0.02

Finland 667 0 0 1 -0.03 6.00 0.66 0.22 0.07

France 1476 0 1 0 -0.02 6.99 0.70 0.16 0.08

UK 4489 0 1 1 -0.01 5.83 0.62 0.20 0.06

Greece 237 0 0 0 -0.04 6.52 0.83 0.17 0.12

Hungary 42 0 0 0 -0.02 6.92 0.69 0.07 0.01

Ireland 159 1 0 0 -0.01 6.68 0.67 0.22 0.16

Italy 689 0 0 0 -0.03 6.76 0.77 0.21 0.09

Luxembourg 29 0 0 0 -0.01 9.31 0.90 0.17 0.13

Netherlands 508 0 0 1 -0.02 6.85 0.58 0.20 0.08

Poland 239 0 0 0 -0.01 6.23 0.85 0.10 0.00

Portugal 18 0 1 0 0.00 7.74 0.35 0.06 -0.05

Sweden 1060 0 0 0 -0.03 5.92 0.56 0.22 0.04

All EU 12629 -0.02 6.32 0.67 0.19 0.07

USA 12629 -0.017 6.33 0.59 0.32 0.01

Test of Diff.: All EU vs. USA (t-stat) 2.80 0.39 -9.32 23.10 1.08

43

Panel C Correlations

Variables EU ∆SUE Size BM Loss ARET

EU

-0.021 -0.010 0.003 -0.144 0.045

∆SUE -0.017

0.147 -0.126 -0.372 0.404

Size -0.003 0.159

-0.270 -0.318 0.033

BM 0.057 -0.217 -0.267

0.146 0.035

Loss -0.144 -0.376 -0.310 0.193

-0.252

ARET -0.007 0.229 -0.071 0.191 -0.107

Panel A reports the number and percentage of firm-year observations by the Fama-French 12 industry classification. It reflects

5,157 unique firms. Panel B reports the sample breakdown by country, country-level institutional factors (Com = 1 for common

law, Enf = 1 for strong enforcement, and Chg = 1 for strengthening of enforcement), and sample means of the variables used in

the regressions by country for ∆SUE, Size, BM, Loss, and ARET. T-statistics for tests of differences in means for firms from all

EU countries vs. firms from the US are shown at the bottom of the panel, with those that are significant at the five percent level

(two-tail) shown in bold. Panel D reports correlations (Pearson correlations are shown below the main diagonal and Spearman

correlations are shown above). Correlations significant at the five percent level (two-tail) are shown in bold. Variable definitions

are in Appendix B.

44

Table 3 Regression Results for Overall Mean ERCs based on Legal Tradition,

Strength of Enforcement, and Change in Reporting Enforcement (H1)

Panel A Regression Results based on Legal Tradition

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

COMMON×∆SUE ab1 -0.45 -0.82 -0.71 0.11

(0.39) (0.18) (0.38) (0.81)

CODE×∆SUE ab2 -0.85* -0.72 -1.20* -0.01

(0.06) (0.16) (0.09) (0.97)

∆SUE 4.10*** 6.28*** 5.56*** 2.40***

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.15 0.16 -0.08 0.32***

(0.12) (0.42) (0.62) (0.00)

BM×∆SUE 0.08 -0.47 0.33 -0.37*

(0.72) (0.32) (0.21) (0.07)

Loss×∆SUE -3.50*** -5.11*** -4.24*** -2.38***

(0.00) (0.00) (0.00) (0.00)

b1-b2 0.40 -0.10 0.49 0.12

(0.14) (0.85) (0.28) (0.72)

COMMON=1 4,648 1,611 1,089 1,948

CODE=1 7,981 2,189 1,920 3,872

Observations 25,258 7,600 6,018 11,640

Adj. R-squared 0.18 0.20 0.26 0.14

The table present regression results of abnormal returns on EU×ΔSUE and other control variables based on Equation (1). Country

and year fixed effects are included but not tabled. See Appendix B for variable definitions. ***, **, * (0.01, 0.05, 0.10 levels,

with p-values in parentheses), two-tail tests. Standard errors are clustered by firm. Panel A is for common law (Com = 1) vs. code

law countries (see Table 2, Panel B); Panel B is for high (Enf = 1) vs. low enforcement countries; and Panel C (Chg = 1) for

countries that strengthened vs. those that did not change their enforcement strength (b1 – b2) is a two-tail test of the single linear

restriction that b1 – b2 = 0, which has a t-distribution where 𝑡 = (𝑏1̂ − 𝑏2̂)/𝑆𝑡𝑑(𝑏1̂ − 𝑏2̂).

45

Panel B Regression Results based on Strength of Enforcement

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

H_ENFORCE×∆SUE ab1 -0.55 -0.80 -0.92 0.19

(0.27) (0.16) (0.25) (0.66)

L_ENFORCE×∆SUE ab2 -0.79* -0.56 -1.09 -0.10

(0.08) (0.31) (0.12) (0.77)

∆SUE 4.14*** 6.23*** 5.62*** 2.38***

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.14 0.16 -0.10 0.32***

(0.16) (0.40) (0.52) (0.00)

BM×∆SUE 0.09 -0.50 0.34 -0.37*

(0.69) (0.29) (0.20) (0.07)

Loss×∆SUE -3.51*** -5.07*** -4.25*** -2.37***

(0.00) (0.00) (0.00) (0.00)

b1-b2 0.24 -0.24 0.17 0.29

(0.21) (0.65) (0.72) (0.36)

H_ENFORCE=1 5,983 1,898 1,444 2,641

L_ENFORCE=1 6,279 1,857 1,500 2,922

Observations 25,258 7,600 6,018 11,640

Adj. R-squared 0.17 0.20 0.25 0.14

Panel C Regression Results based on Change in Reporting Enforcement

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

CHG×∆SUE ab1 -0.66 -0.97* -0.81 0.04

(0.18) (0.06) (0.26) (0.91)

NCHG×∆SUE ab2 -0.87* -0.42 -1.52** -0.02

(0.06) (0.53) (0.04) (0.96)

∆SUE 4.27*** 6.46*** 5.72*** 2.44***

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.12 0.13 -0.10 0.31***

(0.19) (0.49) (0.53) (0.00)

BM×∆SUE 0.07 -0.51 0.33 -0.36*

(0.73) (0.28) (0.20) (0.08)

Loss×∆SUE -3.55*** -5.13*** -4.35*** -2.39***

(0.00) (0.00) (0.00) (0.00)

b1-b2 0.21 -0.49 0.71 0.06

(0.37) (0.38) (0.11) (0.82)

CHG=1 7,257 2,392 1,710 3,155

NCHG=1 5,372 1,408 1,299 2,665

Observations 25,258 7,600 6,018 11,640

Adj. R-squared 0.18 0.20 0.26 0.14

46

Table 4 Regression Results based on Legal Tradition

Panel A R&D (H2) (one-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

COMMON×IND×∆SUE b1 1.15* -1.55 1.70* 0.95

(0.06) (0.95) (0.09) (0.11)

CODE×IND×∆SUE b2 0.76 -0.13 1.53* 0.12

(0.13) (0.57) (0.09) (0..42)

COMMON×∆SUE b3 -0.85 -0.48 -1.37 -0.21

(0.21) (0.45) (0.22) (0.70)

CODE×∆SUE b4 -1.09* -0.78 -1.74* -0.07

(0.06) (0.17) (0.08) (0.87)

IND×∆SUE -0.81 0.83 -1.45 -0.18

(0.20) (0.21) (0.15) (0.74)

∆SUE 4.48*** 6.25*** 6.48*** 2.40***

(0.00) (0.00) (0.00) (0.01)

Size×∆SUE 0.13 0.13 -0.14 0.32***

(0.22) (0.49) (0.44) (0.00)

BM×∆SUE 0.07 -0.42 0.30 -0.35*

(0.74) (0.38) (0.26) (0.10)

Loss×∆SUE -3.51*** -5.16*** -4.31*** -2.36***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) 0.39 -1.42* 0.17 0.83

(0.38) (0.10) (0.83) (0.19)

Observations 25,258 7,600 6,018 11,640

IND=1 & COMMON=1 975 323 231 421

IND=1 & CODE=0 2,591 667 617 1,307

Adj. R-squared 0.18 0.20 0.26 0.15

47

Panel B LIFO (H3) (one-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

COMMON×IND×∆SUE b1 1.10 0.09 2.59 -1.09

(0.80) (0.54) (0.91) (0.16)

CODE×IND×∆SUE b2 0.33 -0.65 1.06 -1.03**

(0.61) (0.25) (0.73) (0.04)

COMMON×∆SUE b3 -0.63 -0.82 -1.16 0.22

(0.23) (0.19) (0.18) (0.66)

CODE×∆SUE b4 -0.91** -0.60 -1.43* 0.11

(0.05) (0.25) (0.06) (0.76)

IND×∆SUE -1.14 0.15 -2.21 0.74

(0.32) (0.79) (0.20) (0.18)

∆SUE 4.32*** 6.29*** 6.41*** 2.35***

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.18* 0.15 -0.06 0.31***

(0.08) (0.42) (0.73) (0.00)

BM×∆SUE 0.08 -0.45 0.32 -0.37*

(0.72) (0.34) (0.22) (0.08)

Loss×∆SUE -3.70*** -5.13*** -4.93*** -2.38***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) 0.77 0.74 1.53 -0.06

(0.26) (0.49) (0.15) (0.96)

Observations 25,258 7,600 6,018 11,640

IND=1 & COMMON=1 653 228 157 268

IND=1 & CODE=0 1,406 408 333 665

Adj. R-squared 0.18 0.20 0.27 0.14

Panel C Software (H4) (two-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

COMMON×IND×∆SUE b1 0.94 2.12 1.35 -0.50

(0.27) (0.18) (0.17) (0.79)

CODE×IND×∆SUE b2 1.15* 0.59 2.31*** -0.06

(0.07) (0.72) (0.01) (0.96)

COMMON×∆SUE b3 -0.52 -1.11* -0.78 0.15

(0.36) (0.06) (0.36) (0.74)

CODE×∆SUE b4 -0.92* -0.82 -1.32* 0.01

(0.06) (0.11) (0.08) (0.97)

IND×∆SUE -0.37 -0.81 -0.58 0.20

(0.52) (0.56) (0.41) (0.85)

∆SUE 4.12*** 6.58*** 5.53*** 2.34***

48

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.15 0.12 -0.06 0.33***

(0.13) (0.47) (0.68) (0.00)

BM×∆SUE 0.08 -0.47 0.33 -0.37*

(0.72) (0.31) (0.21) (0.07)

Loss×∆SUE -3.48*** -5.12*** -4.22*** -2.39***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) -0.21 1.53 -0.96 -0.44

(0.77) (0.15) (0.22) (0.79)

Observations 25,258 7,600 6,018 11,640

IND=1 & COMMON=1 320 121 65 134

IND=1 & CODE=0 564 164 134 266

Adj. R-squared 0.18 0.20 0.26 0.14

Panel D Lease (H4) (two-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

COMMON×IND×∆SUE b1 -0.65 -0.31 0.87 -3.31*

(0.66) (0.75) (0.62) (0.08)

CODE×IND×∆SUE b2 -0.68 -2.73*** -0.85 -1.27

(0.60) (0.00) (0.61) (0.32)

COMMON×∆SUE b3 -0.39 -0.81 -0.79 0.45

(0.46) (0.18) (0.34) (0.29)

CODE×∆SUE b4 -0.77* -0.45 -1.11 0.13

(0.09) (0.41) (0.13) (0.70)

IND×∆SUE 0.46 1.78** 0.38 1.46

(0.72) (0.02) (0.81) (0.24)

∆SUE 4.10*** 6.25*** 5.62*** 2.19***

(0.00) (0.00) (0.00) (0.01)

Size×∆SUE 0.14 0.14 -0.10 0.32***

(0.14) (0.47) (0.54) (0.00)

BM×∆SUE 0.06 -0.51 0.31 -0.39*

(0.78) (0.27) (0.23) (0.05)

Loss×∆SUE -3.50*** -5.06*** -4.25*** -2.27***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) 0.03 2.42*** 1.69* -2.04

(0.98) (0.00) (0.07) (0.16)

Observations 25,258 7,600 6,018 11,640

IND=1 & COMMON=1 550 192 131 227

IND=1 & CODE=0 711 201 177 333

Adj. R-squared 0.18 0.20 0.26 0.15

49

Panel E Goodwill (H4) (two-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

COMMON×IND×∆SUE b1 1.50 -0.06 3.47 0.30

(0.41) (0.97) (0.12) (0.88)

CODE×IND×∆SUE b2 1.72 -0.04 2.10 1.47

(0.26) (0.96) (0.30) (0.25)

COMMON×∆SUE b3 -0.64 -0.82 -1.06 0.14

(0.23) (0.18) (0.22) (0.76)

CODE×∆SUE b4 -1.03** -0.70 -1.48* -0.07

(0.03) (0.19) (0.05) (0.84)

IND×∆SUE -1.94 -0.58 -2.21 -1.75

(0.20) (0.35) (0.26) (0.16)

∆SUE 4.43*** 6.22*** 6.18*** 2.43***

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.16 0.18 -0.08 0.35***

(0.11) (0.35) (0.61) (0.00)

BM×∆SUE 0.07 -0.46 0.31 -0.38*

(0.75) (0.32) (0.24) (0.07)

Loss×∆SUE -3.70*** -5.10*** -4.61*** -2.45***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) -0.22 -0.02 1.37 -1.17

(0.83) (0.99) (0.25) (0.45)

Observations 25,258 7,600 6,018 11,640

IND=1 & COMMON=1 531 192 134 205

IND=1 & CODE=0 807 225 193 389

Adj. R-squared 0.18 0.20 0.26 0.15

Table 4 presents regression results of abnormal returns on Common×IND×ΔSUE, Code×IND×ΔSUE, and other control variables

based on Equation (2). Country and year fixed effects are included but not tabled. See Appendix B for variable definitions.

***, **, * (0.01, 0.05, 0.10 levels, with p-values in parentheses), two-tail tests except for the test variables in Panels A and B,

which are one-tail tests. Standard errors are clustered by firm. Panel A is for R&D, Panel B is for LIFO, Panel C is for multiple-

element software revenue recognition, Panel D is for leasing, and Panel E is for goodwill. Country-level legal tradition is

common law (Com = 1) vs. code law countries (see Table 2, Panel B). (b1 – b2) is a two-tail test of the single linear restriction

that b1 – b2 = 0, which has a t-distribution where 𝑡 = (𝑏1̂ − 𝑏2̂)/𝑆𝑡𝑑(𝑏1̂ − 𝑏2̂).

50

Table 5 Regression Results based on Strength of Enforcement

Panel A R&D (H2) (one-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

H_ENFORCE×IND×∆SUE b1 0.77 -1.62 1.25 0.55

(0.14) (0.96) (0.15) (0.21)

L_ENFORCE×IND×∆SUE b2 0.95* -0.04 1.58* 0.50

(0.08) (0.52) (0.08) (0.18)

H_ENFORCE×∆SUE b3 -0.82 -0.46 -1.43 -0.03

(0.21) (0.44) (0.20) (0.96)

L_ENFORCE×∆SUE b4 -1.11* -0.69 -1.64* -0.28

(0.05) (0.26) (0.08) (0.49)

IND×∆SUE -0.84 0.84 -1.43 -0.29

(0.18) (0.20) (0.15) (0.57)

∆SUE 4.55*** 6.19*** 6.52*** 2.48***

(0.00) (0.00) (0.00) (0.01)

Size×∆SUE 0.11 0.14 -0.17 0.31***

(0.29) (0.47) (0.37) (0.00)

BM×∆SUE 0.08 -0.44 0.31 -0.36*

(0.73) (0.36) (0.25) (0.08)

Loss×∆SUE -3.52*** -5.14*** -4.29*** -2.35***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) -0.19 -1.58** -0.32 0.05

(0.66) (0.05) (0.65) (0.92)

Observations 25,258 7,600 6,018 11,640

IND=1 & H_ENFORCE=1 1,377 400 332 645

IND=1 & L_ENFORCE=0 2,134 585 507 1,042

Adj. R-squared 0.18 0.20 0.26 0.14

51

Panel B LIFO (H3) (one-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

H_ENFORCE×IND×∆SUE b1 1.25 -0.00 3.28 -1.24*

(0.85) (0.50) (0.97) (0.08)

L_ENFORCE×IND×∆SUE b2 0.14 -0.59 0.57 -0.88*

(0.55) (0.28) (0.63) (0.06)

H_ENFORCE×∆SUE b3 -0.75 -0.80 -1.53* 0.32

(0.14) (0.18) (0.08) (0.48)

L_ENFORCE×∆SUE b4 -0.82* -0.45 -1.24* 0.01

(0.07) (0.43) (0.10) (0.99)

IND×∆SUE -1.15 0.15 -2.28 0.68

(0.31) (0.80) (0.18) (0.20)

∆SUE 4.38*** 6.25*** 6.55*** 2.33***

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.16 0.15 -0.08 0.32***

(0.11) (0.41) (0.61) (0.00)

BM×∆SUE 0.08 -0.49 0.33 -0.36*

(0.70) (0.30) (0.21) (0.09)

Loss×∆SUE -3.72*** -5.09*** -4.98*** -2.37***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) 1.11** 0.59 2.71*** -0.36

(0.04) (0.58) (0.00) (0.62)

Observations 25,258 7,600 6,018 11,640

IND=1 & H_ENFORCE=1 860 288 214 358

IND=1 & L_ENFORCE=0 1,090 336 256 498

Adj. R-squared 0.18 0.20 0.26 0.14

Panel C Software (H4) (two-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

H_ENFORCE×IND×∆SUE b1 0.95 1.80 1.45* 0.03

(0.19) (0.25) (0.10) (0.98)

L_ENFORCE×IND×∆SUE b2 1.04 0.59 2.25** -0.17

(0.13) (0.72) (0.02) (0.89)

H_ENFORCE×∆SUE b3 -0.62 -1.05* -1.00 0.20

(0.25) (0.06) (0.23) (0.62)

L_ENFORCE×∆SUE b4 -0.85* -0.66 -1.19 -0.07

(0.08) (0.24) (0.11) (0.81)

IND×∆SUE -0.33 -0.79 -0.47 0.17

(0.56) (0.56) (0.50) (0.87)

∆SUE 4.16*** 6.52*** 5.61*** 2.33***

52

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.14 0.13 -0.09 0.33***

(0.17) (0.45) (0.55) (0.00)

BM×∆SUE 0.09 -0.51 0.34 -0.36*

(0.69) (0.28) (0.20) (0.07)

Loss×∆SUE -3.49*** -5.08*** -4.23*** -2.38***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) -0.09 1.21 -0.80 0.20

(0.88) (0.27) (0.31) (0.86)

Observations 25,258 7,600 6,018 11,640

IND=1 & H_ENFORCE=1 462 150 104 208

IND=1 & L_ENFORCE=0 405 132 91 182

Adj. R-squared 0.17 0.20 0.25 0.14

Panel D Lease (H4) (two-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

H_ENFORCE×IND×∆SUE b1 -0.49 -0.47 0.81 -3.01*

(0.74) (0.62) (0.64) (0.08)

L_ENFORCE×IND×∆SUE b2 -0.77 -2.79*** -0.81 -1.26

(0.56) (0.00) (0.63) (0.32)

H_ENFORCE×∆SUE b3 -0.50 -0.78 -0.97 0.48

(0.32) (0.17) (0.23) (0.22)

L_ENFORCE×∆SUE b4 -0.71 -0.26 -1.00 0.03

(0.11) (0.65) (0.17) (0.93)

IND×∆SUE 0.45 1.77** 0.35 1.43

(0.72) (0.02) (0.83) (0.24)

∆SUE 4.15*** 6.19*** 5.69*** 2.20***

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.13 0.14 -0.12 0.32***

(0.17) (0.46) (0.44) (0.00)

BM×∆SUE 0.07 -0.55 0.32 -0.39*

(0.74) (0.23) (0.22) (0.05)

Loss×∆SUE -3.51*** -5.01*** -4.25*** -2.26***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) 0.28 2.31*** 1.62* -1.75

(0.72) (0.00) (0.07) (0.18)

Observations 25,258 7,600 6,018 11,640

IND=1 & H_ENFORCE=1 685 227 168 290

IND=1 & L_ENFORCE=0 549 165 135 249

Adj. R-squared 0.18 0.21 0.25 0.15

53

Panel E Goodwill (H4) (two-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

H_ENFORCE×IND×∆SUE b1 0.94 0.03 2.53 -0.51

(0.55) (0.99) (0.24) (0.73)

L_ENFORCE×IND×∆SUE b2 1.64 -0.14 2.01 1.04

(0.27) (0.87) (0.32) (0.32)

H_ENFORCE×∆SUE b3 -0.69 -0.81 -1.25 0.25

(0.18) (0.16) (0.15) (0.54)

L_ENFORCE×∆SUE b4 -0.97** -0.53 -1.34* -0.17

(0.03) (0.36) (0.07) (0.62)

IND×∆SUE -1.81 -0.57 -2.20 -1.18

(0.21) (0.36) (0.27) (0.25)

∆SUE 4.44*** 6.16*** 6.26*** 2.34***

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.15 0.18 -0.11 0.35***

(0.14) (0.33) (0.50) (0.00)

BM×∆SUE 0.08 -0.49 0.32 -0.34*

(0.71) (0.29) (0.23) (0.10)

Loss×∆SUE -3.69*** -5.06*** -4.62*** -2.42***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) -0.70 0.16 0.52 -1.56

(0.34) (0.91) (0.61) (0.15)

Observations 25,258 7,600 6,018 11,640

IND=1 & H_ENFORCE=1 679 230 175 274

IND=1 & L_ENFORCE=0 624 182 146 296

Adj. R-squared 0.18 0.20 0.26 0.14

Table 5 presents regression results of abnormal returns on H_ENFORCE×IND×ΔSUE, L_ENFORCE×IND×ΔSUE, and other

control variables based on Equation (2). Country and year fixed effects are included but not tabled. See Appendix B for variable

definitions. ***, **, * (0.01, 0.05, 0.10 levels, with p-values in parentheses), two-tail tests. Standard errors are clustered by firm.

Panel A is for R&D, Panel B is for LIFO, Panel C is for multiple-element software revenue recognition, Panel D is for leasing,

and Panel E is for goodwill. Country-level strength of enforcement is for high (Enf = 1) vs. low enforcement countries (see Table

2, Panel B). (b1 – b2) is a two-tail test of the single linear restriction that b1 – b2 = 0, which has a t-distribution where 𝑡 = (𝑏1̂ −

𝑏2̂)/𝑆𝑡𝑑(𝑏1̂ − 𝑏2̂).

54

Table 6 Regression Results based on Change in Reporting Enforcement

Panel A R&D (H2) (one-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

CHG×IND×∆SUE b1 0.76 -1.01 1.34 0.59

(0. 14) (0.89) (0.13) (0.17)

NCHG×IND×∆SUE b2 0.83 0.18 1.48* 0.09

(0.11) (0.42) (0.09) (0.44)

CHG×∆SUE b3 -0.91 -0.76 -1.30 -0.19

(0.15) (0.17) (0.21) (0.71)

NCHG×∆SUE b4 -1.15* -0.44 -2.03** -0.08

(0.05) (0.54) (0.04) (0.85)

IND×∆SUE -0.82 0.84 -1.46 -0.18

(0.19) (0.20) (0.15) (0.74)

∆SUE 4.65*** 6.42*** 6.62*** 2.48***

(0.00) (0.00) (0.00) (0.01)

Size×∆SUE 0.10 0.10 -0.16 0.31***

(0.32) (0.58) (0.38) (0.00)

BM×∆SUE 0.06 -0.45 0.31 -0.35

(0.77) (0.34) (0.25) (0.10)

Loss×∆SUE -3.55*** -5.17*** -4.39*** -2.38***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) -0.07 -1.19 -0.14 0.50

(0.85) (0.18) (0.81) (0.27)

Observations 25,258 7,600 6,018 11,640

IND=1 & CHG=1 2,044 634 487 645

IND=1 & NCHG=0 1,522 356 361 805

Adj. R-squared 0.18 0.20 0.26 0.14

55

Panel B LIFO (H3) (one-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

CHG×IND×∆SUE b1 0.30 0.10 1.02 -1.17*

(0.60) (0.55) (0.71) (0.05)

NCHG×IND×∆SUE b2 0.75 -1.40 2.16 -0.90*

(0.738) (0.13) (0.88) (0.07)

CHG×∆SUE b3 -0.74 -0.97* -1.03 0.18

(0.13) (0.07) (0.17) (0.68)

NCHG×∆SUE b4 -0.99** -0.07 -1.94** 0.09

(0.04) (0.91) (0.02) (0.82)

IND×∆SUE -1.15 0.18 -2.26 0.74

(0.32) (0.76) (0.19) (0.18)

∆SUE 4.49*** 6.51*** 6.59*** 2.38***

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.15 0.12 -0.08 0.30***

(0.13) (0.52) (0.63) (0.00)

BM×∆SUE 0.07 -0.48 0.33 -0.36*

(0.73) (0.30) (0.21) (0.09)

Loss×∆SUE -3.75*** -5.17*** -5.04*** -2.39***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) -0.45 1.50 -1.13 -0.27

(0.42) (0.23) (0.24) (0.61)

Observations 25,258 7,600 6,018 11,640

IND=1 & CHG=1 1,100 363 265 358

IND=1 & NCHG=0 959 273 225 461

Adj. R-squared 0.18 0.20 0.27 0.14

Panel C Software (H4) (two-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

CHG×IND×∆SUE b1 0.89 1.55 1.77** -0.98

(0.22) (0.31) (0.04) (0.53)

NCHG×IND×∆SUE b2 1.24* 0.84 2.24*** 0.38

(0.05) (0.60) (0.01) (0.75)

CHG×∆SUE b3 -0.72 -1.16** -0.91 0.10

(0.16) (0.02) (0.23) (0.79)

NCHG×∆SUE b4 -0.94* -0.57 -1.63** -0.01

(0.05) (0.40) (0.04) (0.97)

IND×∆SUE -0.38 -0.84 -0.56 0.20

(0.51) (0.54) (0.43) (0.85)

∆SUE 4.29*** 6.72*** 5.71*** 2.38***

56

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.12 0.10 -0.09 0.32***

(0.20) (0.54) (0.56) (0.00)

BM×∆SUE 0.07 -0.51 0.33 -0.36*

(0.74) (0.28) (0.20) (0.07)

Loss×∆SUE -3.52*** -5.13*** -4.33*** -2.40***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) -0.35 0.71 -0.47 -1.35

(0.49) (0.46) (0.45) (0.25)

Observations 25,258 7,600 6,018 11,640

IND=1 & CHG=1 570 203 123 208

IND=1 & NCHG=0 314 82 76 156

Adj. R-squared 0.18 0.20 0.26 0.14

Panel D Lease (H4) (two-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

CHG×IND×∆SUE b1 -0.27 -1.24 1.09 -2.44

(0.84) (0.21) (0.53) (0.11)

NCHG×IND×∆SUE b2 -1.01 -3.05*** -1.11 -1.32

(0.44) (0.00) (0.51) (0.32)

CHG×∆SUE b3 -0.62 -0.90* -0.83 0.30

(0.21) (0.08) (0.26) (0.42)

NCHG×∆SUE b4 -0.76 -0.00 -1.38* 0.13

(0.10) (1.00) (0.08) (0.70)

IND×∆SUE 0.47 1.80** 0.39 1.46

(0.71) (0.02) (0.81) (0.24)

∆SUE 4.27*** 6.47*** 5.75*** 2.25***

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.12 0.10 -0.11 0.30***

(0.21) (0.59) (0.46) (0.00)

BM×∆SUE 0.06 -0.53 0.32 -0.38*

(0.79) (0.25) (0.22) (0.06)

Loss×∆SUE -3.54*** -5.08*** -4.34*** -2.27***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) 0.74 1.81** 2.20** -1.12

(0.26) (0.05) (0.02) (0.27)

Observations 25,258 7,600 6,018 11,640

IND=1 & CHG=1 737 244 174 290

IND=1 & NCHG=0 524 149 134 241

Adj. R-squared 0.18 0.20 0.26 0.15

57

Panel E Goodwill (H4) (two-tailed)

Dependent Variable (1) (2) (3) (4)

ARET 2005~13 2005~07 2008~09 2010~13

CHG×IND×∆SUE b1 1.17 -0.23 2.12 0.34

(0.47) (0.80) (0.30) (0.83)

NCHG×IND×∆SUE b2 2.07 0.16 2.44 1.72

(0.18) (0.86) (0.24) (0.18)

CHG×∆SUE b3 -0.81* -0.95* -1.09 0.06

(0.10) (0.07) (0.15) (0.88)

NCHG×∆SUE b4 -1.10** -0.43 -1.84** -0.11

(0.02) (0.54) (0.02) (0.76)

IND×∆SUE -1.96 -0.56 -2.25 -1.75

(0.20) (0.36) (0.25) (0.16)

∆SUE 4.59*** 6.40*** 6.35*** 2.48***

(0.00) (0.00) (0.00) (0.00)

Size×∆SUE 0.14 0.15 -0.10 0.34***

(0.17) (0.41) (0.53) (0.00)

BM×∆SUE 0.07 -0.50 0.32 -0.36*

(0.76) (0.29) (0.22) (0.08)

Loss×∆SUE -3.75*** -5.12*** -4.73*** -2.49***

(0.00) (0.00) (0.00) (0.00)

(b1-b2) -0.89 -0.40 -0.32 -1.39

(0.16) (0.66) (0.74) (0.17)

Observations 25,258 7,600 6,018 11,640

IND=1 & CHG=1 809 266 199 274

IND=1 & NCHG=0 529 151 128 250

Adj. R-squared 0.18 0.20 0.26 0.15

Table 6 presents regression results of abnormal returns on CHG×IND×ΔSUE, NCHG×IND×ΔSUE, and other control variables

based on Equation (2). Country and year fixed effects are included but not tabled. See Appendix B for variable definitions.

***, **, * (0.01, 0.05, 0.10 levels, with p-values in parentheses), two-tail tests. Standard errors are clustered by firm. Panel A is

for R&D, Panel B is for LIFO, Panel C is for multiple-element software revenue recognition, Panel D is for leasing, and Panel E

is for goodwill. Change in country-level strength of enforcement is for change (Chg = 1) vs. no change in enforcement countries

(see Table 2, Panel B). (b1 – b2) is a two-tail test of the single linear restriction that b1 – b2 = 0, which has a t-distribution where

𝑡 = (𝑏1̂ − 𝑏2̂)/𝑆𝑡𝑑(𝑏1̂ − 𝑏2̂).